Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric.
Ijaems apr-2016-23 Study of Pruning Techniques to Predict Efficient Business ...INFOGAIN PUBLICATION
The shopping mall domain is a dynamic and unpredictable environment. Traditional techniques such as fundamental and technical analysis can provide investors with some tools for managing their shops and predicting their business growth. However, these techniques cannot discover all the possible relations between business growth and thus, there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the shopping malls and help to increase business growth. Therefore, there is a need to find a perfect solution or an algorithm to work with this kind of environment. So we are going to study few methods of pruning with decision tree. Finally, we prove and make use of the Cost based pruning method to obtain an objective evaluation of the tendency to over prune or under prune observed in each method.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric.
Ijaems apr-2016-23 Study of Pruning Techniques to Predict Efficient Business ...INFOGAIN PUBLICATION
The shopping mall domain is a dynamic and unpredictable environment. Traditional techniques such as fundamental and technical analysis can provide investors with some tools for managing their shops and predicting their business growth. However, these techniques cannot discover all the possible relations between business growth and thus, there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the shopping malls and help to increase business growth. Therefore, there is a need to find a perfect solution or an algorithm to work with this kind of environment. So we are going to study few methods of pruning with decision tree. Finally, we prove and make use of the Cost based pruning method to obtain an objective evaluation of the tendency to over prune or under prune observed in each method.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
There are two basic types of decision tree analysis: Classification and Regression, Classification Trees are used when the target variable is categorical and used to classify/divide data into these predefined categories. Regression Trees are used when the target variable is numeric. Decision Tree analysis is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.
This presentation discusses the application of discriminant analysis in sports research. One can understand the steps involved in the analysis and testing its assumptions.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Presentation:
1. What is Machine Learning?
2. Types of Machine Learning?
3. Problems in Machine Learning
4. What is Decision Tree?
5. What are the problems a Decision Tree Solves?
6. Advantages of Decision Tree
7. How does Decision Tree Work?
8. Use Case - Loan Repayment Prediction
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
PRIORITIZING THE BANKING SERVICE QUALITY OF DIFFERENT BRANCHES USING FACTOR A...ijmvsc
In recent years, India’s service industry is developing rapidly. The objective of the study is to explore the
dimensions of customer perceived service quality in the context of the Indian banking industry. In order to
categorize the customer needs into quality dimensions, Factor analysis (FA) has been carried out on
customer responses obtained through questionnaire survey. Analytic Hierarchy Process (AHP) is employed
to determine the weights of the banking service quality dimensions. The priority structure of the quality
dimensions provides an idea for the Banking management to allocate the resources in an effective manner
to achieve more customer satisfaction. Technique for Order Preference Similarity to Ideal Solution
(TOPSIS) is used to obtain final ranking of different branches.
There are two basic types of decision tree analysis: Classification and Regression, Classification Trees are used when the target variable is categorical and used to classify/divide data into these predefined categories. Regression Trees are used when the target variable is numeric. Decision Tree analysis is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.
This presentation discusses the application of discriminant analysis in sports research. One can understand the steps involved in the analysis and testing its assumptions.
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Presentation:
1. What is Machine Learning?
2. Types of Machine Learning?
3. Problems in Machine Learning
4. What is Decision Tree?
5. What are the problems a Decision Tree Solves?
6. Advantages of Decision Tree
7. How does Decision Tree Work?
8. Use Case - Loan Repayment Prediction
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
PRIORITIZING THE BANKING SERVICE QUALITY OF DIFFERENT BRANCHES USING FACTOR A...ijmvsc
In recent years, India’s service industry is developing rapidly. The objective of the study is to explore the
dimensions of customer perceived service quality in the context of the Indian banking industry. In order to
categorize the customer needs into quality dimensions, Factor analysis (FA) has been carried out on
customer responses obtained through questionnaire survey. Analytic Hierarchy Process (AHP) is employed
to determine the weights of the banking service quality dimensions. The priority structure of the quality
dimensions provides an idea for the Banking management to allocate the resources in an effective manner
to achieve more customer satisfaction. Technique for Order Preference Similarity to Ideal Solution
(TOPSIS) is used to obtain final ranking of different branches.
1. Quality management questionnaire
In this file, you can ref useful information about quality management questionnaire such as
quality management questionnaireforms, tools for quality management questionnaire, quality
management questionnairestrategies … If you need more assistant for quality management
questionnaire, please leave your comment at the end of file.
Other useful material for quality management questionnaire:
• qualitymanagement123.com/23-free-ebooks-for-quality-management
• qualitymanagement123.com/185-free-quality-management-forms
• qualitymanagement123.com/free-98-ISO-9001-templates-and-forms
• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of quality management questionnaire
==================
God would like to thank you for your belief and patronage. In order to better serve your needs,
He asks that you take a few moments to answer the following questions.
Please keep in mind that your responses will be kept completely confidential, and that you need
not disclose your name or address unless you prefer a direct response to comments or
suggestions.
1. How did you find out about your Deity?
___ Newspaper
___ Bible
___ Torah
___ Book of Mormon
___ Koran
___ Divine inspiration
___ Dead Sea Scrolls
___ My mama done tol' me
___ Near-death experience
2. ___ Near-life experience
___ National Public Radio
___ Tabloid
___ Burning shrubbery
___ Other (specify): _____________
2. Which model Deity did you acquire?
___ Yahweh
___
Father, Son & Holy Ghost [Trinity
Pak]
___ Jehovah
___ Jesus
___ Krishna
___ Zeus and entourage [Olympus Pak]
___ Odin and entourage [Valhalla Pak]
___ Allah
___ Satan
___ Gaia/Mother Earth/Mother Nature
___ God 1.0a (hairy thunderer)
___ God 1.0b (cosmic muffin)
___
None of the above; I was taken in by a
false god
3. Did your God come to you undamaged,
with all parts in good working order and
with no obvious breakage or missing
attributes?
___ Yes
___ No
If no, please describe the problems you
3. initially encountered here. Please indicate all
that apply:
___ Not eternal
___
Finite in space/Does not occupy or
inhabit the entire cosmos
___ Not omniscient
___ Not omnipotent
___
Not infinitely plastic (incapable of
being all things to all creations)
___ Permits sex outside of marriage
___ Prohibits sex outside of marriage
___
Makes mistakes (Geraldo Rivera,
Jesse Helms)
___
Makes or permits bad things to
happen to good people
___
When beseeched, doesn't stay
beseeched
___ Requires burnt offerings
___ Requires virgin sacrifices
___ Plays dice with the universe
4. What factors were relevant in your
decision to acquire a Deity? Please check all
that apply.
___ Indoctrinated by parents
___ Needed a reason to live
___ Indoctrinated by society
___ Needed focus in whom to despise
___ Imaginary friend grew up
___
Wanted to know Jesus in the Biblical
sense
___ Graduated from the tooth fairy
4. ___ Hate to think for myself
___ Wanted to meet girls/boys
___ Fear of death
___ Wanted to piss off parents
___ Needed a day away from work
___ Desperate need for certainty
___ Like organ music
___ Need to feel morally superior
___ Thought Jerry Falwell was cool
___
My shrubbery caught fire and told me
to do it
5. Have you ever worshipped a Deity
before? If so, which false god were you
fooled by? Please check all that apply.
___ Mick Jagger
___ Rajanish
___ Baal
___ The almighty dollar
___ Bill Gates
___ Left-wing liberalism
___ The radical right
___ Ra
___ Beelzebub
___ Barney T.B.P.D.
___ The Great Spirit
___ The Great Pumpkin
___ The sun
___ Elvis
___ Cindy Crawford
5. ___ The moon
___ TV news
___ Burning shrubbery
___ Other: ________________
6. Are you currently using any other source of inspiration
in addition to God? Please check all that apply.
__ Tarot __ Lottery __ Astrology
__ Television
__ Fortune
cookies
__ Ann
Landers
__ Psychic Friends Network
__
Dianetics
__ Palmistry
__ Playboy and/orPlaygirl
__ Self-
help books
__ Sex, drugs,
rock and roll
__ Biorhythms __ Alcohol __ Bill Clinton
__ Tea leaves __ EST
__
CompuServe
__ Mantras
__ Jimmy
Swaggert
__ Crystals
(not including
Crystal Gayle)
__ Human sacrifice
__
Pyramids
__ Wandering
in a desert
__ Burning shrubbery
__ Barney
T.B.P.D.
__ Barney Fife
Other:___________
7. God employs a limited degree of divine intervention to preserve the balanced level of felt
presence and blind faith. Which would you prefer (circle one)?
a. More divine intervention
b. Less divine intervention
c. Current level of divine intervention is just right
d. Don't know...what's divine intervention?
6. 8. God also attempts to maintain a balanced level of disasters and miracles. Please rate on a scale
of 1 - 5 his handling of the following
(1=unsatisfactory, 5=excellent):
Disasters:
flood 1 2 3 4 5
famine 1 2 3 4 5
earthquake 1 2 3 4 5
war 1 2 3 4 5
pestilence 1 2 3 4 5
plague 1 2 3 4 5
spam 1 2 3 4 5
AOL 1 2 3 4 5
Miracles:
rescues 1 2 3 4 5
spontaneous remissions 1 2 3 4 5
stars hovering over jerkwater towns 1 2 3 4 5
crying statues 1 2 3 4 5
water changing to wine 1 2 3 4 5
walking on water 1 2 3 4 5
VCRs that set their own clocks 1 2 3 4 5
Saddam Hussein still alive 1 2 3 4 5
getting any sex whatsoever 1 2 3 4 5
9. Do you have any additional comments or suggestions for improving the quality of God's
services? (Attach an additional sheet if necessary.)
If you are able to complete the questionnaire and return it to one of our conveniently located
drop-off boxes by October 31 you will be entered in the One Free Miracle of Your Choice
drawing (chances of winning are approximately one in 6.023 x 10 to the 23rd power, depending
on number of beings entered).
==================
7. III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
Who filled out the check sheet
What was collected (what each check represents,
an identifying batch or lot number)
Where the collection took place (facility, room,
apparatus)
When the collection took place (hour, shift, day
of the week)
Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
8. to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
9. The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
10. being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
People: Anyone involved with the process
Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
Machines: Any equipment, computers, tools, etc.
required to accomplish the job
Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
Measurements: Data generated from the process
that are used to evaluate its quality
11. Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
III. Other topics related to Quality management questionnaire (pdf download)
quality management systems
quality management courses
quality management tools
iso 9001 quality management system
quality management process
quality management system example
quality system management
quality management techniques
quality management standards