Application of Feature Selection Methods in Educational Data Mining

Acharya, Anal; Sinha, Devadatta

doi:10.5120/18048-8951

Cited by 19 publications

(17 citation statements)

References 13 publications

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“…Some of these are reported in section 1. A notable difference between those works and current work is that here feature selection is explored in detail [18] before performing classification. Various models developed may be farther compared on the basis of the following parameters: (ii) Number of classes predicted: This gives the number of categories in to which data is to be classified.…”

Section: Comparative Study With Contemporary Litteraturementioning

confidence: 94%

“…Classifying data with such a large attribute set may lead to several disadvantages [10]: high computational complexity, poor model interpretability and high data over fitting that would reduce generalization. Acharya and Sinha [18] have performed a detailed study on the effect of several Filter and Wrapper based feature selection techniques on DS1. Three filter based methods used were Correlation Based Feature Selection (CBFS), Chi-Square Based Feature Evaluation (CBFS) and Information Gain Attribute Evaluation (IGATE).…”

Section: Resultsmentioning

confidence: 99%

“…As discussed in the previous section five MLAs from 5 classes are chosen (C4.5, MLP, NB, 1-NN, SMO) for predicting student results. In continuation of [18] two parameters [17] are used to determine the degree of predictive accuracy: Kappa statistic and F-Measure. Kappa statistic measures the agreement of prediction with true class: a value of 1.0 signifies complete agreement.…”

Section: Resultsmentioning

confidence: 99%

“…F-measure is a combination of Precision and Recall parameters. Again in continuation to [18] the experiments for this paper have been conducted in WEKA (Waikato Environment for Knowledge Analysis) which is free software [19] written in Java for data analysis and predictive modeling. The values of Kappa Statistic and F-Measure corresponding to the MLAs for DS1 and DS2 are shown in Tables 3,4,5,6 respectively.…”

Section: Resultsmentioning

confidence: 99%

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Early Prediction of Students Performance using Machine Learning Techniques

Acharya¹,

Sinha²

2014

IJCA

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In recent years Educational Data Mining (EDM) has emerged as a new field of research due to the development of several statistical approaches to explore data in educational context. One such application of EDM is early prediction of student results. This is necessary in higher education for identifying the "weak" students so that some form of remediation may be organized for them. In this paper a set of attributes are first defined for a group of students majoring in Computer Science in some undergraduate colleges in Kolkata. Since the numbers of attributes are reasonably high, feature selection algorithms are applied on the data set to reduce the number of features. Five classes of Machine Learning Algorithm (MLA) are then applied on this data set and it was found that the best results were obtained with the decision tree class of algorithms. It was also found that the prediction results obtained with this model are comparable with other previously developed models.

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Section: Comparative Study With Contemporary Litteraturementioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Early Prediction of Students Performance using Machine Learning Techniques

Acharya¹,

Sinha²

2014

IJCA

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“…Feature selection has been done using filter-based feature selection method. There are several filter-based feature selection algorithms [29,30]. The Relief Based Feature Selection, Entropy Based Feature Selection and Correlation based feature selection was used.…”

Section: Feature Selectionmentioning

confidence: 99%

A novel academic performance estimation model using two stage feature selection

Chaudhury¹,

Tripathy

2020

IJEECS

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<span lang="EN-GB">Educational data mining has gained tremendous interest from researchers across the globe. Using data mining techniques in the field of education several significant findings have been made. Accurate academic performance estimation is a challenging task. In this study we have developed a novel model to estimate the academic performance of students. Techniques like conversion of categorical attributes into dummy variables, classification, two staged feature selection and an improved differential evolutionary algorithm were used. Our proposed model outperformed existing models of students’ academic performance determination and gave a new direction to it. The proposed model can help not only to reduce the number of academic failures but also help to comprehend the factors contributing to a student’s academic performance (poor, average or outstanding).Computer</span>

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