Efficient and effective feature discovery for CART decision tree model

Bachelor thesis (2022)

Authors

A.B.C. Bien Electrical Engineering, Mathematics and Computer Science

Contributors

R. Hai Web Information Systems - EEMCS (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Benedict Bien
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Published Date

22-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract


A common challenge in feature discovery and feature selection is the trade-off between effectiveness and efficiency. The paper proposes a solution that is efficient and effective at ranking features for feature discovery.
This paper aims to improve feature discovery techniques, by estimating the overall utility of features, through ranking them by their characteristics, such as the correlation coefficient, gini impurity, information gain, etc. The approach to estimate the overall utility is done by calculating the likelihoods of a feature being selected with a wrapper feature selection technique, given their ranking with respect to their characteristics. The likelihoods of the rankings are recorded and combined to estimate the overall utility of a feature which is used to rank all the features by their utility.