CN109409644A - A kind of student performance analysis method based on improved C4.5 algorithm - Google Patents
A kind of student performance analysis method based on improved C4.5 algorithm Download PDFInfo
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- 238000003066 decision tree Methods 0.000 claims abstract description 25
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- 238000004364 calculation method Methods 0.000 claims description 29
- 230000011218 segmentation Effects 0.000 claims description 13
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Abstract
The present invention relates to a kind of student performance analysis methods based on improved C4.5 algorithm, belong to Data Mining Classification technical field.First establish the database of a student performance；Secondly, the student achievement data to acquisition pre-processes；Then, improved C4.5 algorithm is introduced, by the information gain-ratio of each attribute in calculate node, the maximum node of ratio of profit increase is then set as root node, and the building of decision tree is carried out to pretreated student achievement data collection；Finally, analyzing the decision tree of student performance building, multiple subject achievements of student are excavated, excavate the internal relation hidden between subject, find out the related causes for influencing student examination achievement.Compared with prior art, the present invention mainly providing the student performance analysis method based on improved C4.5 algorithm, the method for the present invention science is practical, can rapidly and accurately find out the related causes for influencing student examination achievement.
Description
Technical field
The present invention relates to a kind of student performance analysis methods based on improved C4.5 algorithm, belong to Data Mining Classification skill
Art field.
Background technique
The increased enrollment of China Higher school, the size of the student body of each school increasingly come big, the expansion of scale and number of student
Increase the Campus MIS of colleges and universities made to face huge challenge.
Improved C4.5 algorithm is the algorithm that a kind of pair of data are analyzed and classify, and is increased by first calculating its information of each attribute
Beneficial rate, then ratio of profit increase is compared, the maximum attribute root node thus is obtained, decision is carried out to pretreated student achievement data collection
The building of tree；The decision tree of student performance building is analyzed, multiple subjects in relation to student performance are excavated, is excavated
The internal relation hidden between student performance and each subject achievement out.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of student performance analysis method based on improved C4.5 algorithm,
The internal relation hidden between each attribute can be rapidly and accurately excavated, the correlation original for influencing student examination achievement is found out
Cause.
The technical scheme is that a kind of student performance analysis method based on improved C4.5 algorithm, specific steps
Are as follows:
Step1, complete student result data database is established；
Step2, the pre-processing that student performance is cleaned and is converted；
The score of each subject of Step3, statistic calculate separately the information gain-ratio of each subject achievement；
The C4.5 algorithm building student performance analysis model of Step4, application enhancements；
Step5, it is excavated by the achievement to student, finds contacting between the total marks of the examination of student and correlated curriculum.
Further, in the step in Step1, student result data database is the collection with student result data
It closes, field includes student's student number and score of each subject.
Further, the step Step2 is pre-processed to student result data, including to not being inconsistent in information data
Or missing data is cleaned and is converted, there are missing values for collected student achievement data, it is therefore desirable to carry out to data
Cleaning, in order to construct simplified decision tree branches, needs Continuous valued attributes sliding-model control, to collected student performance
Data carry out conversion process, and student result data is converted into the different grade of outstanding, good, qualifying, difference.
Further, the step Step3 is statistical sample data, calculates separately its information gain-ratio to each attribute:
If sample has different Category Attributes, different attribute has different values, selects A for testing attribute, if attribute S contains
niA positive example and piA counter-example, then shown in comentropy E (A) calculation formula such as formula (1) of attribute A:
In formula, niIt is the positive number of cases in attribute S, piIt is the counter-example number in attribute S, attribute S believes relative to the segmentation of attribute A
Breath amount is Split (S, A), shown in calculation formula such as formula (2) of the attribute S relative to the segmentation information amount of attribute A:
In formula, niIt is S in different values, the number of attribute A value, selecting the foundation of attribute root node is attribute
Information gain-ratio is maximum, shown in the calculation formula such as formula (3) of the information gain-ratio Gain (S, A) of attribute S:
By comparing above calculated result analysis it is found that the ratio of profit increase of course T is maximum, i.e. information contained by T is to this
The influence of example classification is maximum, so using the maximum attribute T of ratio of profit increase as root node.
Further, the step Step4 is the C4.5 algorithm building student performance analysis model of application enhancements；It is generating
It is divided into leaf node by the various situations of T with recursive method building decision tree again after using course T as root node.Again by passing
The method returned calculates the information gain of each attribute of each node, goes out other attributes by recursive calculation using same method
Information gain-ratio simultaneously generates branch node just producible decision tree, and statistical sample data calculate separately its information to each attribute and increase
Beneficial rate；Assuming that sample has S different Category Attributes, different attribute has different values, selects A for testing attribute, it is assumed that attribute S
In contain niPositive example and piA counter-example, then comentropy E (A) calculation formula such as formula (1) of attribute A, attribute S is relative to attribute A
Segmentation information amount Split (S, A) calculation formula such as formula (2), the calculating of the information gain-ratio Gain (S, A) of attribute S is public
Formula such as formula (3) goes out the information gain-ratio of other attributes by recursive calculation using same method and generates branch's leaf node
Just produce decision tree.
Further, the step Step5 is and to generate decision tree by carrying out data mining to association attributes variable and obtain
To classifying rules, correlation between each attribute variable can be obtained by analyzing the above classifying rules, passed through the achievement to student and dug
Pick, finds contacting between the total marks of the examination of student and correlated curriculum.
The beneficial effects of the present invention are: compared with prior art, the present invention mainly providing based on improved C4.5 algorithm
Student performance analysis method, the method for the present invention science is practical, can rapidly and accurately find out influence student examination achievement phase
Close reason.
Detailed description of the invention
Fig. 1 is flow chart of steps of the present invention；
Fig. 2 is step Step2 flow chart of the present invention；
Fig. 3 is step Step3 flow chart of the present invention；
Fig. 4 is that given example is based on improved C4.5 algorithm building student performance analysis mould in the specific embodiment of the invention
The flow chart of type.
Specific embodiment
With reference to the accompanying drawings and detailed description, the invention will be further described.
Embodiment 1: as shown in Figure 1-3, a kind of student performance analysis method based on improved C4.5 algorithm, first establishes one
The database of a student performance；Secondly, the student achievement data to acquisition pre-processes；Then, improved C4.5 is introduced to calculate
Method, by the information gain-ratio of each attribute in calculate node, the maximum node of ratio of profit increase is then set as root node, to pre- place
The student achievement data collection managed carries out the building of decision tree；Finally, analyzing the decision tree of student performance building, to
Raw multiple subject achievements are excavated, and the internal relation hidden between subject is excavated, and finding out influences student examination achievement
Related causes.
Specific steps are as follows:
Step1, complete student result data database is established；
Step2, the pre-processing that student performance is cleaned and is converted；
The score of each subject of Step3, statistic calculate separately the information gain-ratio of each subject achievement；
The C4.5 algorithm building student performance analysis model of Step4, application enhancements；
Step5, it is excavated by the achievement to student, finds contacting between the total marks of the examination of student and correlated curriculum.
Further, in the step in Step1, student result data database is the collection with student result data
It closes, field includes student's student number and score of each subject.
Further, the step Step2 is pre-processed to student result data, including to not being inconsistent in information data
Or missing data is cleaned and is converted, there are missing values for collected student achievement data, it is therefore desirable to carry out to data
Cleaning, in order to construct simplified decision tree branches, needs Continuous valued attributes sliding-model control, to collected student performance
Data carry out conversion process, and student result data is converted into the different grade of outstanding, good, qualifying, difference.
Further, the step Step3 is statistical sample data, calculates separately its information gain-ratio to each attribute:
If sample has different Category Attributes, different attribute has different values, selects A for testing attribute, if attribute S contains
niA positive example and piA counter-example, then shown in comentropy E (A) calculation formula such as formula (1) of attribute A:
In formula, niIt is the positive number of cases in attribute S, piIt is the counter-example number in attribute S, attribute S believes relative to the segmentation of attribute A
Breath amount is Split (S, A), shown in calculation formula such as formula (2) of the attribute S relative to the segmentation information amount of attribute A:
In formula, niIt is S in different values, the number of attribute A value, selecting the foundation of attribute root node is attribute
Information gain-ratio is maximum, shown in the calculation formula such as formula (3) of the information gain-ratio Gain (S, A) of attribute S:
By comparing above calculated result analysis it is found that the ratio of profit increase of course T is maximum, i.e. information contained by T is to this
The influence of example classification is maximum, so using the maximum attribute T of ratio of profit increase as root node.
Further, the step Step4 is the C4.5 algorithm building student performance analysis model of application enhancements；It is generating
It is divided into leaf node by the various situations of T with recursive method building decision tree again after using course T as root node.Again by passing
The method returned calculates the information gain of each attribute of each node, goes out other attributes by recursive calculation using same method
Information gain-ratio simultaneously generates branch node just producible decision tree, and statistical sample data calculate separately its information to each attribute and increase
Beneficial rate；Assuming that sample has S different Category Attributes, different attribute has different values, selects A for testing attribute, it is assumed that attribute S
In contain niPositive example and piA counter-example, then comentropy E (A) calculation formula such as formula (1) of attribute A, attribute S is relative to attribute A
Segmentation information amount Split (S, A) calculation formula such as formula (2), the calculating of the information gain-ratio Gain (S, A) of attribute S is public
Formula such as formula (3) goes out the information gain-ratio of other attributes by recursive calculation using same method and generates branch's leaf node
Just produce decision tree.
Further, the step Step5 is and to generate decision tree by carrying out data mining to association attributes variable and obtain
To classifying rules, correlation between each attribute variable can be obtained by analyzing the above classifying rules, passed through the achievement to student and dug
Pick, finds contacting between the total marks of the examination of student and correlated curriculum.
Embodiment 2: as shown in Figs 1-4, planting the student performance analysis method based on improved C4.5 algorithm, the method
Specific step is as follows:
Step1, complete student result data database is established；Specifically:
There is following course: C programmer design, assembler language, Introduction to Computers, object-oriented in tentative built database
C++, database .Net programming, are indicated with C1, C2, C3, C4, C5 and C6.
Step2, the pre-processing that student performance is cleared up and is converted；Specifically:
There are missing values for collected student achievement data, it is therefore desirable to clean to data.In order to construct simplification
Decision tree branches, first will to student performance carry out it is discrete, to collected student achievement data carry out conversion process.?
Raw performance information is converted into the different grade of outstanding, good, qualifying, difference, is indicated with A, B, C and D.
Step3, statistic achievement calculate separately its information gain-ratio to each subject；Specifically:
It is whether excellent as category attribute using .Net programming achievement, it counts each subject and is set in test subject .Net program
The number of positive example and counter-example under meter.Calculate each section's purpose comentropy E (A), comentropy E (A) the calculation formula such as formula of attribute A
(1)。
niIt is the positive number of cases in attribute S, piIt is the counter-example number in attribute S, whether attribute A indicates .Net programming achievement
It is excellent.Attribute S is Split (S, A), segmentation information amount of the attribute S relative to attribute A relative to the segmentation information amount of attribute A
Calculation formula such as formula (2).
niIt is S in different values, the number of attribute A value.The foundation of selection attribute root node is the information of attribute
Ratio of profit increase is maximum.The calculation formula such as formula (3) of the information gain-ratio Gain (S, A) of attribute S.
By comparing above calculated result it is found that the ratio of profit increase of discovery course C1 is maximum, that is to say, that contained by course C1
The influence that some information classifies to this example is the largest, so using the maximum course C1 of ratio of profit increase as root attribute, so with class
Journey C1 is root node.
The C4.5 algorithm building student performance analysis method of Step4, application enhancements；Specifically:
Decision tree is constructed with recursive method again after generating using course C1 as root node, by the various classification categories of course C1
Property, that is, it is divided into leaf node.The information gain of each leaf node under course C1 is calculated by recursive method again.Take information gain
The maximum leaf node of rate is as new branch node and then generates decision tree.Statistic achievement, to each attribute point of course C1
Its information gain-ratio is not calculated；Select A for testing attribute, it is assumed that contain a n in attribute SiPositive example and piA counter-example, then attribute A
Comentropy E (A) calculation formula such as formula (1).Attribute S is Split (S, A), attribute S phase relative to the segmentation information amount of attribute A
For the calculation formula such as formula (2) of the segmentation information amount of attribute A, the calculation formula of the information gain-ratio Gain (S, A) of attribute S
Such as formula (3).
" information gain-ratio " of other attributes is gone out by recursive calculation using same method and generates branch's leaf node.Again
Respectively to the information gain-ratio of each branch's leaf node carry out calculate and after generate decision tree.
Step5, classifying rules can be acquired by decision tree, it, can by the analysis of achievement, lesson data to student
To find the correlation between course and student examination achievement and course offered；Specifically:
By being excavated to specialized course and the student achievement data of leading class, specialized course and the correlated curriculum of specialized course,
And generate decision tree and obtain classifying rules, the correlation between each course can be obtained by the above classifying rules.
In conjunction with attached drawing, the embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (6)
1. a kind of student performance analysis method based on improved C4.5 algorithm, it is characterised in that:
Step1, complete student result data database is established；
Step2, the pre-processing that student performance is cleaned and is converted；
The score of each subject of Step3, statistic calculate separately the information gain-ratio of each subject achievement；
The C4.5 algorithm building student performance analysis model of Step4, application enhancements；
Step5, it is excavated by the achievement to student, finds contacting between the total marks of the examination of student and correlated curriculum.
2. the student performance analysis method according to claim 1 based on improved C4.5 algorithm, it is characterised in that: described
In step in Step1, student result data database is the set with student result data, and field includes student's student number and each
Section's achievement.
3. the student performance analysis method according to claim 1 based on improved C4.5 algorithm, it is characterised in that: described
Step Step2 be student result data is pre-processed, including to be not inconsistent in information data or missing data carry out cleaning and
Conversion, needs Continuous valued attributes sliding-model control, carries out conversion process to collected student achievement data, student at
Achievement information is converted into the different grade of outstanding, good, qualifying, difference.
4. the student performance analysis method according to claim 1 based on improved C4.5 algorithm, it is characterised in that: described
Step Step3 is statistical sample data, calculates separately its information gain-ratio to each attribute:
If sample has different Category Attributes, different attribute has different values, selects A for testing attribute, if attribute S contains niIt is a
Positive example and piA counter-example, then shown in comentropy E (A) calculation formula such as formula (1) of attribute A:
In formula, niIt is the positive number of cases in attribute S, piIt is the counter-example number in attribute S, segmentation information amount of the attribute S relative to attribute A
For Split (S, A), shown in calculation formula such as formula (2) of the attribute S relative to the segmentation information amount of attribute A:
In formula, niIt is S in different values, the foundation of the number of attribute A value, selection attribute root node is the information of attribute
Ratio of profit increase is maximum, shown in the calculation formula such as formula (3) of the information gain-ratio Gain (S, A) of attribute S:
By comparing above calculated result analysis it is found that the ratio of profit increase of course T is maximum, i.e. information contained by T is to this example point
The influence of class is maximum, so using the maximum attribute T of ratio of profit increase as root node.
5. the student performance analysis method according to claim 1 based on improved C4.5 algorithm, it is characterised in that: described
Step Step4 is the C4.5 algorithm building student performance analysis model of application enhancements；It is used again after generating using course T as root node
Recursive method building decision tree is divided into leaf node by the various situations of T.Each node is calculated by recursive method again
The information gain of each attribute goes out the information gain-ratio of other attributes by recursive calculation using same method and generates branch
Node just produces decision tree, and statistical sample data calculate separately its information gain-ratio to each attribute；Assuming that sample has S a not
Same Category Attributes, different attribute have different values, select A for testing attribute, it is assumed that contain a n in attribute SiPositive example and piIt is a
Counter-example, then comentropy E (A) calculation formula such as formula (1) of attribute A, segmentation information amount Split of the attribute S relative to attribute A
The calculation formula of (S, A) such as formula (2), the calculation formula such as formula (3) of the information gain-ratio Gain (S, A) of attribute S, using same
The method of sample, which goes out the information gain-ratio of other attributes by recursive calculation and generates branch's leaf node, just produces decision tree.
6. the student performance analysis method according to claim 1 based on improved C4.5 algorithm, it is characterised in that: described
Step Step5 is and to generate decision tree by carrying out data mining to association attributes variable and obtain classifying rules, above point of analysis
Rule-like can obtain the correlation between each attribute variable, excavated by the achievement to student, find the total marks of the examination of student
Contacting between correlated curriculum.
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| CN111199314A (en) * | 2019-12-30 | 2020-05-26 | 成都康赛信息技术有限公司 | Method for analyzing factors influencing scores of middle school students based on C4.5 algorithm |
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| CN113283694A (en) * | 2021-04-15 | 2021-08-20 | 国网河北省电力有限公司培训中心 | Power distribution network operation assessment method and system |
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Application publication date: 20190301 |