This document introduces a new ranking approach called Manifold Ranking with Sink Points (MRSP) that addresses diversity, relevance, and importance in ranking. MRSP uses a manifold ranking process over data to find the most relevant objects while also using ranked objects as sink points to prevent redundant objects from receiving high ranks. MRSP was applied to update summarization and query recommendation tasks, where it performed better than existing ranking approaches by achieving strong empirical results.
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
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RUNNING HEAD: EFFICIENTLY MODEL UNCERTAINTY IN ML AND NLP, AND UNCERTAINTY RESULTING FROM BIG DATA ANALYTICS
Efficiently model uncertainty in ML and NLP, and uncertainty resulting from Big Data Analytics
ITS 836
Data Science & Big Data Analytics
Submitted by
Prof: Dr.
Date: May 31th, 2020
Introduction
When dealing with big data analytics, ML is commonly used to make models for forecast and knowledge detection to empower information-driven dynamics.
Conventional ML techniques are not computationally efficient or adaptable enough to deal with both the attributes of big data (eg, huge volumes, high speeds,
shifting sorts, low-value density, inadequacy) and vulnerability (eg, inclined training data, surprising information types, and so forth.). A few usually utilized impelled
ML procedures proposed for enormous information examination incorporate element learning, profound learning, move learning, circulated learning, and dynamic
1
2
3
4
5
Source Matches (23)
learning (Ghavami, 2019). Feature learning includes a lot of methods that empower a framework to naturally find the portrayals required for include recognition or
classification from unprocessed information. Examining Analytics Techniques
The performances of the ML algorithms are firmly influenced by choice of information depiction. Deep learning algorithms are intended for breaking down and
removing vital information from large measures of data and information gathered from different sources (eg, separate varieties within a picture, for example, a light,
different materials, and shapes), nevertheless current deep learning models acquire a high computational expense. Distributed learning can be utilized to
moderate the adaptability issue of customary ML via completing computations on informational indexes appropriated among a few workstations to scale up the
learning procedure. Transfer learning is the ability to use information acquired from one setting to a new setting, effectively improving a student from one area by
moving data from a related space. Dynamic learning alludes to calculations that utilize versatile information collection (i.e., forms that consequently alter parameters
to gather the most helpful information as fast as could reasonably be expected) so as to quicken ML exercises and overcome naming issues (Dasgupta, 2018). The
vulnerability difficulties .
%67
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%1
SafeAssign Originality Report
%69Total Score: High risk
Submission UUID: f4c068ff-4928-cd77-e068-bcb8cc87644f
Total Number of Reports
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69 %
Submitted on
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02:49 PM EDT
Average Word Count
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Highest:
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Institutional database (7)
Student paper Student paper Student paper
My paper Student paper Student paper
Student paper
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springer b-ok
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Student paper
Top sources (3)
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Word Count: 1,332
5 2 6
3 1 8
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5 Student paper 2 Student paper 6 Student paper
RUNNING HEAD: EFFICIENTLY MODEL UNCERTAINTY IN ML AND NLP, AND UNCERTAINTY RESULTING FROM BIG DATA ANALYTICS
Efficiently model uncertainty in ML and NLP, and uncertainty resulting from Big Data Analytics
ITS 836
Data Science & Big Data Analytics
Submitted by
Prof: Dr.
Date: May 31th, 2020
Introduction
When dealing with big data analytics, ML is commonly used to make models for forecast and knowledge detection to empower information-driven dynamics.
Conventional ML techniques are not computationally efficient or adaptable enough to deal with both the attributes of big data (eg, huge volumes, high speeds,
shifting sorts, low-value density, inadequacy) and vulnerability (eg, inclined training data, surprising information types, and so forth.). A few usually utilized impelled
ML procedures proposed for enormous information examination incorporate element learning, profound learning, move learning, circulated learning, and dynamic
1
2
3
4
5
Source Matches (23)
learning (Ghavami, 2019). Feature learning includes a lot of methods that empower a framework to naturally find the portrayals required for include recognition or
classification from unprocessed information. Examining Analytics Techniques
The performances of the ML algorithms are firmly influenced by choice of information depiction. Deep learning algorithms are intended for breaking down and
removing vital information from large measures of data and information gathered from different sources (eg, separate varieties within a picture, for example, a light,
different materials, and shapes), nevertheless current deep learning models acquire a high computational expense. Distributed learning can be utilized to
moderate the adaptability issue of customary ML via completing computations on informational indexes appropriated among a few workstations to scale up the
learning procedure. Transfer learning is the ability to use information acquired from one setting to a new setting, effectively improving a student from one area by
moving data from a related space. Dynamic learning alludes to calculations that utilize versatile information collection (i.e., forms that consequently alter parameters
to gather the most helpful information as fast as could reasonably be expected) so as to quicken ML exercises and overcome naming issues (Dasgupta, 2018). The
vulnerability difficulties .
On multi dimensional cubes of census data: designing and queryingJaspreet Issaj
The primary focus of this research is to design a data warehouse that specifically targets OLAP storage, analyzing and querying requirements to the multidimensional cubes of census data with an efficient and timely manner.
More Related Content
Similar to Dotnet ranking on data manifold with sink points
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
%67
%1
%1
SafeAssign Originality Report
%69Total Score: High risk
Submission UUID: f4c068ff-4928-cd77-e068-bcb8cc87644f
Total Number of Reports
1
Highest Match
69 %
Average Match
69 %
Submitted on
05/31/20
02:49 PM EDT
Average Word Count
1,332
Highest:
%69Attachment 1
Institutional database (7)
Student paper Student paper Student paper
My paper Student paper Student paper
Student paper
Internet (2)
springer b-ok
Global database (1)
Student paper
Top sources (3)
Excluded sources (0)
Word Count: 1,332
5 2 6
3 1 8
4
10 9
7
5 Student paper 2 Student paper 6 Student paper
RUNNING HEAD: EFFICIENTLY MODEL UNCERTAINTY IN ML AND NLP, AND UNCERTAINTY RESULTING FROM BIG DATA ANALYTICS
Efficiently model uncertainty in ML and NLP, and uncertainty resulting from Big Data Analytics
ITS 836
Data Science & Big Data Analytics
Submitted by
Prof: Dr.
Date: May 31th, 2020
Introduction
When dealing with big data analytics, ML is commonly used to make models for forecast and knowledge detection to empower information-driven dynamics.
Conventional ML techniques are not computationally efficient or adaptable enough to deal with both the attributes of big data (eg, huge volumes, high speeds,
shifting sorts, low-value density, inadequacy) and vulnerability (eg, inclined training data, surprising information types, and so forth.). A few usually utilized impelled
ML procedures proposed for enormous information examination incorporate element learning, profound learning, move learning, circulated learning, and dynamic
1
2
3
4
5
Source Matches (23)
learning (Ghavami, 2019). Feature learning includes a lot of methods that empower a framework to naturally find the portrayals required for include recognition or
classification from unprocessed information. Examining Analytics Techniques
The performances of the ML algorithms are firmly influenced by choice of information depiction. Deep learning algorithms are intended for breaking down and
removing vital information from large measures of data and information gathered from different sources (eg, separate varieties within a picture, for example, a light,
different materials, and shapes), nevertheless current deep learning models acquire a high computational expense. Distributed learning can be utilized to
moderate the adaptability issue of customary ML via completing computations on informational indexes appropriated among a few workstations to scale up the
learning procedure. Transfer learning is the ability to use information acquired from one setting to a new setting, effectively improving a student from one area by
moving data from a related space. Dynamic learning alludes to calculations that utilize versatile information collection (i.e., forms that consequently alter parameters
to gather the most helpful information as fast as could reasonably be expected) so as to quicken ML exercises and overcome naming issues (Dasgupta, 2018). The
vulnerability difficulties .
%67
%1
%1
SafeAssign Originality Report
%69Total Score: High risk
Submission UUID: f4c068ff-4928-cd77-e068-bcb8cc87644f
Total Number of Reports
1
Highest Match
69 %
Average Match
69 %
Submitted on
05/31/20
02:49 PM EDT
Average Word Count
1,332
Highest:
%69Attachment 1
Institutional database (7)
Student paper Student paper Student paper
My paper Student paper Student paper
Student paper
Internet (2)
springer b-ok
Global database (1)
Student paper
Top sources (3)
Excluded sources (0)
Word Count: 1,332
5 2 6
3 1 8
4
10 9
7
5 Student paper 2 Student paper 6 Student paper
RUNNING HEAD: EFFICIENTLY MODEL UNCERTAINTY IN ML AND NLP, AND UNCERTAINTY RESULTING FROM BIG DATA ANALYTICS
Efficiently model uncertainty in ML and NLP, and uncertainty resulting from Big Data Analytics
ITS 836
Data Science & Big Data Analytics
Submitted by
Prof: Dr.
Date: May 31th, 2020
Introduction
When dealing with big data analytics, ML is commonly used to make models for forecast and knowledge detection to empower information-driven dynamics.
Conventional ML techniques are not computationally efficient or adaptable enough to deal with both the attributes of big data (eg, huge volumes, high speeds,
shifting sorts, low-value density, inadequacy) and vulnerability (eg, inclined training data, surprising information types, and so forth.). A few usually utilized impelled
ML procedures proposed for enormous information examination incorporate element learning, profound learning, move learning, circulated learning, and dynamic
1
2
3
4
5
Source Matches (23)
learning (Ghavami, 2019). Feature learning includes a lot of methods that empower a framework to naturally find the portrayals required for include recognition or
classification from unprocessed information. Examining Analytics Techniques
The performances of the ML algorithms are firmly influenced by choice of information depiction. Deep learning algorithms are intended for breaking down and
removing vital information from large measures of data and information gathered from different sources (eg, separate varieties within a picture, for example, a light,
different materials, and shapes), nevertheless current deep learning models acquire a high computational expense. Distributed learning can be utilized to
moderate the adaptability issue of customary ML via completing computations on informational indexes appropriated among a few workstations to scale up the
learning procedure. Transfer learning is the ability to use information acquired from one setting to a new setting, effectively improving a student from one area by
moving data from a related space. Dynamic learning alludes to calculations that utilize versatile information collection (i.e., forms that consequently alter parameters
to gather the most helpful information as fast as could reasonably be expected) so as to quicken ML exercises and overcome naming issues (Dasgupta, 2018). The
vulnerability difficulties .
On multi dimensional cubes of census data: designing and queryingJaspreet Issaj
The primary focus of this research is to design a data warehouse that specifically targets OLAP storage, analyzing and querying requirements to the multidimensional cubes of census data with an efficient and timely manner.
Similar to Dotnet ranking on data manifold with sink points (20)
On multi dimensional cubes of census data: designing and querying
Dotnet ranking on data manifold with sink points
1. RANKING ON DATA MANIFOLD WITH SINK POINTS
ABSTRACT:
Ranking is an important problem in various applications, such as Information Retrieval (IR),
natural language processing, computational biology, and social sciences. Many ranking
approaches have been proposed to rank objects according to their degrees of relevance or
importance. Beyond these two goals, diversity has also been recognized as a crucial criterion in
ranking. Top ranked results are expected to convey as little redundant information as possible,
and cover as many aspects as possible. However, existing ranking approaches either take no
account of diversity, or handle it separately with some heuristics.
In this paper, we introduce a novel approach, Manifold Ranking with Sink Points (MRSPs), to
address diversity as well as relevance and importance in ranking. Specifically, our approach uses
a manifold ranking process over the data manifold, which can naturally find the most relevant
and important data objects. Meanwhile, by turning ranked objects into sink points on data
manifold, we can effectively prevent redundant objects from receiving a high rank. MRSP not
only shows a nice convergence property, but also has an interesting and satisfying optimization
explanation. We applied MRSP on two application tasks, update summarization and query
recommendation, where diversity is of great concern in ranking. Experimental results on both
tasks present a strong empirical performance of MRSP as compared to existing ranking
approaches.
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