- Feature selection is an important step in aviation machine learning that improves model performance by choosing a subset of relevant features from large datasets. It reduces complexity, enhances accuracy and interpretability.
- Aviation data presents unique challenges for feature selection due to its size, high dimensionality from sensors, potential noise, and time-series nature. Domain expertise is important for selecting meaningful features.
- Various filter, wrapper and embedded methods can be used, and the choice depends on the problem, data types, and computational constraints. Future areas of focus include integration with deep learning and ensuring explainability of models.
Dataset: Gather a large dataset of laptops and their features, including processor speed, RAM, storage, and display size, along with their corresponding prices.
Feature engineering: Extracting meaningful features from the dataset, such as brand, model, and year, and transforming them into a format that machine learning algorithms can use.
Model selection: Choosing the most appropriate machine learning algorithm, such as linear regression, decision tree, or random forest, based on the type of data and desired level of accuracy.
Model training: Splitting the dataset into training and testing sets, and using the training data to train the machine learning model.
Model evaluation: Testing the model's performance on the testing data and evaluating its accuracy using metrics such as mean squared error or R-squared.
Hyperparameter tuning: Optimizing the model's hyperparameters, such as learning rate or regularization strength, to achieve the best performance.
Building a mind map for test data management.
Overview
1. Test data source
2. Extract or create data
3. Transform data
4. Provision
5. Target
Source: http://debasishbhadra.blogspot.com/2013/12/create-your-own-mindmap-for-test-data.html
Operational testing with employee performance tracking for compliance CloudMoyo
For railroads, tracking employee performance and ensuring they align with regulatory safety standards, operating procedures are important parts of ensuring safety and security in the organization.
However, without an effective ops testing program and access to consolidated tools for tracking employee accountability due to lack of visibility into employee history (like demerits, drug and alcohol testing failures, or verbal cautions) across locations, Labor Relations, and railroads meeting safety goals becomes challenging.
Find out how railroads today can ensure effective ops testing with performance tracking to build employee accountability for safety compliance. Ashok will share tips to build a robust ops testing program, how to track employee performance through accountability tools and help labor relations to improve safety, including best practices to respond to violations and set up policies to track employee performance.
What is Feature Engineering?
Feature engineering is the process of creating or selecting relevant
features from raw data to improve the performance of machine
learning models.
Feature engineering is the process of transforming raw data into
features that are suitable for machine learning models. In other
words, it is the process of selecting, extracting, and transforming the
most relevant features from the available data to build more accurate
and efficient machine learning models.
In the context of machine learning, features are individual measurable
properties or characteristics of the data that are used as inputs for the
learning algorithms. The goal of feature engineering is to transform the
raw data into a suitable format that captures the underlying patterns
and relationships in the data, thereby enabling the machine learning
model to make accurate predictions or classifications
Dataset: Gather a large dataset of laptops and their features, including processor speed, RAM, storage, and display size, along with their corresponding prices.
Feature engineering: Extracting meaningful features from the dataset, such as brand, model, and year, and transforming them into a format that machine learning algorithms can use.
Model selection: Choosing the most appropriate machine learning algorithm, such as linear regression, decision tree, or random forest, based on the type of data and desired level of accuracy.
Model training: Splitting the dataset into training and testing sets, and using the training data to train the machine learning model.
Model evaluation: Testing the model's performance on the testing data and evaluating its accuracy using metrics such as mean squared error or R-squared.
Hyperparameter tuning: Optimizing the model's hyperparameters, such as learning rate or regularization strength, to achieve the best performance.
Building a mind map for test data management.
Overview
1. Test data source
2. Extract or create data
3. Transform data
4. Provision
5. Target
Source: http://debasishbhadra.blogspot.com/2013/12/create-your-own-mindmap-for-test-data.html
Operational testing with employee performance tracking for compliance CloudMoyo
For railroads, tracking employee performance and ensuring they align with regulatory safety standards, operating procedures are important parts of ensuring safety and security in the organization.
However, without an effective ops testing program and access to consolidated tools for tracking employee accountability due to lack of visibility into employee history (like demerits, drug and alcohol testing failures, or verbal cautions) across locations, Labor Relations, and railroads meeting safety goals becomes challenging.
Find out how railroads today can ensure effective ops testing with performance tracking to build employee accountability for safety compliance. Ashok will share tips to build a robust ops testing program, how to track employee performance through accountability tools and help labor relations to improve safety, including best practices to respond to violations and set up policies to track employee performance.
What is Feature Engineering?
Feature engineering is the process of creating or selecting relevant
features from raw data to improve the performance of machine
learning models.
Feature engineering is the process of transforming raw data into
features that are suitable for machine learning models. In other
words, it is the process of selecting, extracting, and transforming the
most relevant features from the available data to build more accurate
and efficient machine learning models.
In the context of machine learning, features are individual measurable
properties or characteristics of the data that are used as inputs for the
learning algorithms. The goal of feature engineering is to transform the
raw data into a suitable format that captures the underlying patterns
and relationships in the data, thereby enabling the machine learning
model to make accurate predictions or classifications
Data Engineer’s Lunch #67: Machine Learning - Feature SelectionAnant Corporation
In Data Engineer’s Lunch #67: Machine Learning - Feature Selection, we discussed the process of picking particular, relevant data features out of a wider data set, to be used to perform model training.
Data Quality in Test Automation Navigating the Path to Reliable TestingKnoldus Inc.
Data Quality in Test Automation: Navigating the Path to Reliable Testing" delves into the crucial role of data quality within the realm of test automation. It explores strategies and methodologies for ensuring reliable testing outcomes by addressing challenges related to the accuracy, completeness, and consistency of test data. The discussion encompasses techniques for managing, validating, and optimizing data sets to enhance the effectiveness and efficiency of automated testing processes, ultimately fostering confidence in the reliability of software systems.
In this session, TESCO will review the Lessons Learned from AMI Deployments and Asset Management Readiness. One of the main objectives of any AMI smart meter initiative is to provide customers with increased visibility, insight, control, and convenience. The AMI smart meter initiative fundamentally transforms the relationship a utility has with its customers by enabling them to become more self-aware of their energy usage. Your organization’s view of assets under management, and how best to manage and control them, will be paramount to the on-going realization of your investment.
This presentation is intended to give the viewer a working knowledge of the practical applications of SAS in terms of Banking Analytics. Specifically, Enterprise Guide and Enterprise Miner have been discussed in detail.
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
Mainframe Sort Operations: Gaining the Insights You Need for Peak PerformancePrecisely
Mainframe systems remain the backbone of many mission-critical business operations, and sort operations play an integral role in ensuring the smooth flow of data across these systems.
However, managing and optimizing sort operations can be a complex task, often hindered by a lack of visibility and real-time insights.
In this webinar, we'll explore how to gain better visibility into mainframe sort operations, enabling you to: identify and resolve performance bottlenecks, optimize resource allocations and improve overall system performance.
Join us for this webcast to hear about:
• The importance of visibility into mainframe sort operations
• Common challenges faced when managing mainframe sort operations
• Strategies for gaining deeper insights into sort operations
Data Engineer's Lunch #67: Machine Learning - Feature SelectionAnant Corporation
In Data Engineer's Lunch #67, Obioma Anomnachi will discuss the process of feature selection as part of a Machine Learning process. Feature selection describes the process of picking particular, relevant data features out of a wider data set, to be used to perform model training.
Accompanying Blog: Coming Soon!
Accompanying YouTube: https://youtu.be/3CPpoQv2tjU
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
Data Engineer’s Lunch #67: Machine Learning - Feature SelectionAnant Corporation
In Data Engineer’s Lunch #67: Machine Learning - Feature Selection, we discussed the process of picking particular, relevant data features out of a wider data set, to be used to perform model training.
Data Quality in Test Automation Navigating the Path to Reliable TestingKnoldus Inc.
Data Quality in Test Automation: Navigating the Path to Reliable Testing" delves into the crucial role of data quality within the realm of test automation. It explores strategies and methodologies for ensuring reliable testing outcomes by addressing challenges related to the accuracy, completeness, and consistency of test data. The discussion encompasses techniques for managing, validating, and optimizing data sets to enhance the effectiveness and efficiency of automated testing processes, ultimately fostering confidence in the reliability of software systems.
In this session, TESCO will review the Lessons Learned from AMI Deployments and Asset Management Readiness. One of the main objectives of any AMI smart meter initiative is to provide customers with increased visibility, insight, control, and convenience. The AMI smart meter initiative fundamentally transforms the relationship a utility has with its customers by enabling them to become more self-aware of their energy usage. Your organization’s view of assets under management, and how best to manage and control them, will be paramount to the on-going realization of your investment.
This presentation is intended to give the viewer a working knowledge of the practical applications of SAS in terms of Banking Analytics. Specifically, Enterprise Guide and Enterprise Miner have been discussed in detail.
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
Data Quality Management: Cleaner Data, Better Reportingaccenture
In this new Accenture Finance & Risk presentation we explore a process to investigate, prioritize and resolve data quality issues, key to creating a more efficient and accurate reporting environment. View our presentation to learn more.
For more on regulatory reporting, see presentation on Financial Reporting Robotics: http://bit.ly/2qaLK9y
Visit our blog for latest Regulatory Insights: https://accntu.re/2qnXs1B
Mainframe Sort Operations: Gaining the Insights You Need for Peak PerformancePrecisely
Mainframe systems remain the backbone of many mission-critical business operations, and sort operations play an integral role in ensuring the smooth flow of data across these systems.
However, managing and optimizing sort operations can be a complex task, often hindered by a lack of visibility and real-time insights.
In this webinar, we'll explore how to gain better visibility into mainframe sort operations, enabling you to: identify and resolve performance bottlenecks, optimize resource allocations and improve overall system performance.
Join us for this webcast to hear about:
• The importance of visibility into mainframe sort operations
• Common challenges faced when managing mainframe sort operations
• Strategies for gaining deeper insights into sort operations
Data Engineer's Lunch #67: Machine Learning - Feature SelectionAnant Corporation
In Data Engineer's Lunch #67, Obioma Anomnachi will discuss the process of feature selection as part of a Machine Learning process. Feature selection describes the process of picking particular, relevant data features out of a wider data set, to be used to perform model training.
Accompanying Blog: Coming Soon!
Accompanying YouTube: https://youtu.be/3CPpoQv2tjU
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Feature Engineering in Machine LearningKnoldus Inc.
In this Knolx we are going to explore Data Preprocessing and Feature Engineering Techniques. We will also understand what is Feature Engineering and its importance in Machine Learning. How Feature Engineering can help in getting the best results from the algorithms.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
1. This course is prepared under the Erasmus+ KA-210-YOU Project titled
«Skilling Youth for the Next Generation Air Transport Management»
Machine Learning
Applications in Aviation
Feature Selection
Asst. Prof. Dr. Emircan Özdemir
Eskişehir Technical University
2. • In data mining, 80% of the analysis effort is spent on data cleaning and preparation and
only 20% is typically spent on modeling.
• Data cleansing and preparation are things that are better learned through experience and
not so much from a book or a course. Feature selection is one of the important steps that
affects the success of the model during the data preparation phase.
• Feature selection is the process of choosing a subset of relevant features from a larger
set to improve model performance, reduce computational complexity, and enhance
interpretability.
• Aviation datasets often contain a large number of features, including sensor data, flight
parameters, weather conditions, and historical data. Selecting relevant features has
important impacts on improving the accuracy, efficiency, and interpretability of machine
learning models in aviation applications.
• Specific challenges and considerations arise when dealing with aviation data. This may
include the large size of datasets, high dimensionality due to various sensor inputs,
potential noise in data, and the need to handle time-series and sequential data.
Feature Selection 2
Introduction
3. • Large datasets in aviation:
Aviation datasets are expansive, encompassing a wealth of information from flight records,
sensor readings, and historical data. Managing and processing such large volumes of data pose
challenges in terms of storage, processing speed, and computational resources.
• High dimensionality due to various sensor inputs:
Aviation data is characterized by high dimensionality, arising from the diverse set of sensors
capturing parameters like altitude, airspeed, and GPS coordinates. The multitude of sensor
inputs increases the complexity of the data, emphasizing the need for effective feature selection
to enhance model performance.
• Noisy data and potential outliers:
Aviation datasets often contain noise, stemming from sensor inaccuracies or external factors.
Additionally, the presence of potential outliers can impact model training and prediction
accuracy. Implementing preprocessing techniques and outlier detection methods becomes
crucial to ensure the reliability and robustness of machine learning models in aviation
applications.
Feature Selection 3
Aviation Data Characteristics
4. Sensor data:
Sensor data encompasses information collected from various onboard sensors, such as
altitude sensors, airspeed indicators, and GPS devices. These real-time measurements
provide crucial insights into the aircraft's operational status and environmental conditions.
Flight parameters:
Flight parameters include data related to the aircraft's performance and navigation, such as
pitch, roll, and yaw angles. These parameters are essential for understanding the dynamics
of flight and optimizing control systems.
Feature Selection 4
Types of Features in Aviation Data
5. Weather conditions:
Weather conditions feature meteorological data like temperature, humidity, wind speed, and
precipitation. Incorporating this data is vital for assessing potential hazards and optimizing
flight paths in response to changing weather patterns.
Historical data:
Historical data comprises information from past flights, maintenance records, and incidents.
Analyzing historical patterns aids in predicting potential issues, optimizing maintenance
schedules, and enhancing overall safety and efficiency.
Feature Selection 5
Types of Features in Aviation Data
6. Aircraft specifications:
Aircraft specifications include details about the aircraft's make, model, engine type, and
other technical specifications. This information is crucial for tailoring machine learning
models to specific aircraft types and optimizing performance.
Customer/Passenger data:
Customer data may include information about passengers, their preferences, and feedback.
While privacy considerations are paramount, leveraging anonymized customer data can
contribute to personalized services, improved customer experiences, and operational
efficiency.
Feature Selection 6
Types of Features in Aviation Data
7. • Data quality and preprocessing issues:
Ensuring the quality of aviation data is paramount, as inaccuracies and inconsistencies may
arise from sensor malfunctions or external factors. Preprocessing challenges include
cleaning noisy data, handling missing values, and normalizing features to enhance the
reliability of the machine learning models.
• High dimensionality and computational complexity:
Aviation datasets often exhibit high dimensionality due to numerous sensors and diverse
parameters. The computational complexity associated with processing and analyzing such
datasets can be a challenge. Feature selection methods must be efficient to handle the
large number of features without compromising model performance.
Feature Selection 7
Challenges in Aviation Feature Selection
8. • Incorporating domain knowledge:
In aviation, domain expertise is crucial for identifying relevant features and understanding
the intricacies of the data. Integrating domain knowledge into the feature selection process
ensures that selected features align with operational requirements and contribute to
meaningful insights.
• Handling time-series and sequential data:
Aviation data frequently involves time-series and sequential information, such as flight
trajectories and sensor readings over time. Feature selection methods need to account for
the temporal nature of the data, considering how features evolve during different phases of
flight and adapting to the sequential nature of events.
Feature Selection 8
Challenges in Aviation Feature Selection
9. • Remove irrelevant features/attributes
• Increase the performance of your model
• Make the model training faster
• Build your model easier with less and relevant features
• Build models which are easy to to understand
• With less features, it’s easy to debug your models
Feature Selection 9
Advantages of Feature Selection
10. Feature Selection
Filter Methods
Correlation analysis
Information gain
Mutual information
Wrapper Methods
Recursive Feature
Elimination (RFE)
Forward selection
Backward
elimination
Embedded
Methods
LASSO (Least
Absolute Shrinkage
and Selection
Operator)
Decision Trees and
Random Forests
Regularized
regression models
Feature Selection 10
Feature Selection Techniques
11. Filter Methods: Filter methods involve the direct evaluation of individual features without
considering the impact on the model. These methods are computationally efficient and
include:
• Correlation analysis: Assesses the linear relationship between features and identifies
highly correlated ones. It helps in selecting a subset of features that are less redundant.
Suitable for numerical data.
• Information gain: Measures the reduction in uncertainty about the target variable when
considering a particular feature. Features with high information gain are prioritized.
Primarily used for categorical target variables, but can be adapted for numerical data.
• Mutual information: Quantifies the amount of information shared between a feature and
the target variable. It aids in selecting features that contribute significantly to predictive
accuracy. Can be applied to both numerical and categorical data.
Feature Selection 11
Feature Selection Techniques
12. Wrapper Methods: Wrapper methods determine feature subsets based on the model's
performance. These methods involve iterative model training and selection and include:
• Recursive Feature Elimination (RFE): Systematically removes the least important
features by training the model iteratively. RFE helps identify the most critical features for
optimal model performance. Applicable to both numerical and categorical data.
• Forward selection: Builds the feature set incrementally by adding the most relevant
feature in each iteration. It continues until a predefined criterion is met, optimizing for
model accuracy. Typically used with numerical data.
• Backward elimination: Starts with all features and removes the least important ones
iteratively. It aims to find the minimal subset of features that maximizes model
performance. Similar to forward selection, it is often applied to numerical data.
Feature Selection 12
Feature Selection Techniques
13. Embedded Methods: Embedded methods integrate feature selection into the model
training process. These methods include:
• LASSO (Least Absolute Shrinkage and Selection Operator): Introduces a penalty term
during model training that encourages sparsity in feature weights, effectively selecting a
subset of important features. Suitable for numerical data.
• Decision Trees and Random Forests: Built-in feature selection mechanisms within
decision tree algorithms. These models naturally highlight important features based on
their contribution to decision-making. Can handle both numerical and categorical data.
• Regularized regression models: Incorporate regularization terms in regression models,
penalizing the inclusion of unnecessary features. This encourages the selection of
relevant features. Primarily designed for numerical data.
Feature Selection 13
Feature Selection Techniques
14. • Thoroughly understand your dataset, including feature types and inherent patterns.
• Clearly define your goal for feature selection, such as improving accuracy or interpretability.
• Choose methods aligned with your data types—numerical or categorical.
• Assess computational complexity, considering the size of your dataset.
• Use methods like correlation analysis or regularization for correlated features.
• Decide on model-agnostic or model-specific feature selection based on your preference.
• Leverage domain expertise to guide feature selection based on contextual insights.
• Explore ensemble methods like Random Forests, which naturally perform feature selection.
• Assess the stability and consistency of the feature selection method.
• Use cross-validation to ensure selected features generalize well to unseen data.
• Experiment with multiple methods and compare outcomes to identify the most suitable.
• Understand trade-offs between simplicity, accuracy, and computational efficiency.
Feature Selection 14
How to choose the right feature selection technique?
15. Domain-Specific Feature Selection refers to the process of selecting relevant features for a
machine learning model based on the specific knowledge and characteristics of a particular
domain or industry. In other words, it involves tailoring the feature selection process to the
intricacies and requirements of a specific field or domain of expertise.
Key components of Domain-Specific Feature Selection are:
• Domain Knowledge
• Collaboration with Domain Experts
• Custom Criteria for Selection
• Relevance to Industry-Specific Goals
• Enhanced Model Performance
Feature Selection 15
Domain-Specific Feature Selection
16. • Importance of domain knowledge in aviation: You should recognize the critical role of
domain knowledge in aviation feature selection. Experts in the field possess insights into
the significance of certain features and can guide the selection process for optimal model
performance.
• Collaboration with domain experts: The collaboration with aviation domain experts is
invaluable. Working closely with professionals who understand the intricacies of aviation
data ensures that feature selection aligns with operational requirements, safety
considerations, and industry-specific nuances.
• Custom feature selection based on aviation-specific criteria: There is a need for
custom feature selection criteria tailored to aviation. Generic approaches may not capture
the unique aspects of aviation data. Creating bespoke selection criteria based on
industry-specific considerations enhances the relevance and effectiveness of the chosen
features.
Feature Selection 16
Domain-Specific Feature Selection
17. • Optimizing Flight Safety
An airline may implement feature selection to identify critical flight parameters from a vast
array of sensor data. By focusing on key indicators such as altitude, airspeed, and engine
performance, the airline can successfully enhance its predictive models for detecting
potential safety issues. This results in more accurate and timely alerts, contributing to
improved overall flight safety.
• Efficient Aircraft Maintenance Scheduling
An aviation maintenance facility may utilize historical data for predictive maintenance. By
employing feature selection techniques, the team can identify the most relevant features
related to aircraft health and performance. This can streamline the maintenance scheduling
process, reducing downtime and operational costs, while ensuring optimal aircraft reliability.
Feature Selection 17
Case Studies
18. • Enhanced Air Traffic Management
Air traffic control agencies face challenges in processing large volumes of data for optimal
route planning. Feature selection methods can be applied to prioritize weather
conditions, airspace congestion, and historical flight patterns. This enables the
development of more efficient air traffic management systems, reducing delays and
improving overall airspace utilization.
• Fuel Efficiency Improvement
An airline may aim to optimize fuel consumption by identifying the most influential
factors affecting fuel efficiency. Feature selection can be conducted focusing on variables
such as weather conditions, aircraft weight, and engine performance. The resulting model
provides actionable insights, leading to fuel-efficient operational strategies and substantial
cost savings.
Feature Selection 18
Case Studies
19. • Customized Aircraft Design
An aircraft manufacturer may leverage feature selection to identify key specifications for
designing customized aircraft. By considering factors such as passenger preferences,
operational requirements, and fuel efficiency, the company can optimize its design process.
This can result in the production of aircraft that better met the unique needs of specific
markets and clients.
• Enhanced Passenger Experience
An airline may aim to improve the overall passenger experience by tailoring services and
operations to individual preferences. The airline can access to a diverse set of passenger
data, including demographic information, travel history, and in-flight behaviors. By utilizing a
combination of filter and wrapper methods, the airline can identify key features
influencing passenger satisfaction. This can led to the implementation of personalized
services such as tailored in-flight entertainment recommendations, optimized seating
arrangements aligned with passenger preferences, and an efficient onboard retail selection
Feature Selection 19
Case Studies
20. • Integration of deep learning techniques
For enhanced predictive modeling, integration of deep learning techniques in aviation is
becoming important. Deep learning algorithms, with their capacity to automatically extract
intricate patterns from large datasets, hold the potential to improve the accuracy and
efficiency of feature selection, especially in scenarios where complex relationships exist
within the data.
• Explainable AI for aviation applications
Explainable AI (XAI) in aviation has a growing importance for transparent and interpretable
machine learning models. As aviation systems become more reliant on AI, ensuring the
explainability of model decisions becomes crucial for safety, regulatory compliance, and
gaining the trust of industry stakeholders.
Feature Selection 20
Future Trends and Technologies
21. • Advances in real-time feature selection
Real-time feature selection, where models dynamically adapt to changing data conditions, is
an emerging trend. With the advancements in computational capabilities, the ability to
perform feature selection in real-time allows aviation systems to respond promptly to
evolving circumstances, optimizing decision-making processes and enhancing overall
system responsiveness.
Feature Selection 21
Future Trends and Technologies
22. • In RapidMiner, using the Repository window, follow
the path Training Resources-Model-Unsupervised-
Feature Weights and open the Hotel App Select by
Weight Solution process.
• In this example, three different feature selection
methods are provided in the model. These methods
are Information Gain, Correlation, and Relief. All of
these three methods are weighting methods to select
features.
• Data is imported using ETL subprocess.
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RapidMiner Example on Feature Selection
23. • In this model, feature weighting is
implemented in three different ways,
using the Feature Weights
operators.
• Weights are normalized and sorted
in descending order.
- Information Gain
- Correlation (using squared
correlation)
- Relief
• For each of the three sets of
weights, Select by Weights operator
keeps only the most important
attributes (threshold is set to 0.5).
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RapidMiner Example on Feature Selection
24. • You can inspect the outputs using Results view.
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RapidMiner Example on Feature Selection
25. • You can select the most relevant features considering these weights you obtained.
• Also as mentioned earlier, you should use domain expertise in the selection process.
• The selection threshold was set 0.5. You can try different thresholds and make decisions
considering different scenarios.
• Building the model with the right combination of feature set will help you to obtain more
succesful and accurate outputs/predictions.
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RapidMiner Example on Feature Selection
26. • In summary, feature selection is crucial in aviation's data-driven narrative. It's not just a
tool; it's the essence of constructing precise and efficient machine learning models.
Navigating through the challenges posed by complex aviation data, we unveiled smart
strategies to enhance accuracy and efficiency. Tailored feature selection is the game-
changer, shaping a path towards more accurate predictions and optimized aviation
operations.
• Tailoring methodologies to aviation data's unique characteristics not only boosts model
accuracy but also ensures safety and operational efficiency. In aviation, selecting the right
features is like fine-tuning an instrument, orchestrating a harmonious symphony of data
insights.
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Conclusion
27. • Considering dynamic advancements in deep learning and real-time processing;
discussing challenges and collobaration are important. Data scientists, aviation experts,
and researchers, should collobarate to refine feature selection techniques.
Feature Selection 27
Conclusion