Predicting higher secondary results by data mining algorithms with VBR: A feature reduction method

The Higher Secondary Certificate (HSC) Examination in Bangladesh was very crucial for students' future, as well as job lives. As the performance of this examination was influenced by some factors, the main scope of this research was to identify these influencing factors with highest possible precision. In this research, an experimental methodology was adopted to generate a dataset which was constructed from students who passed HSC. Several Data mining methods were used to analyze data, extracting models, and develop knowledge to make decision and also used some feature selection methods to remove unnecessary and redundant features which were helped to improve the performance of the classifiers. Here applied several existent feature selection algorithms and a proposed approach to find the relevant features. The results of the empirical analysis were obtained by removing these irrelevant features with some bayes based, function based, lazy based, rule base and tree base classifiers. And finally the results showed, feature reduction had some effect on performance and for overall LMT classifier with our selected features performed better than others.