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Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification

Gkikas, Dimitris C.; Theodoridis, Prokopis K.; Theodoridis, Theodoros; Gkikas, Marios C.

Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification Thumbnail


Authors

Dimitris C. Gkikas

Prokopis K. Theodoridis

Marios C. Gkikas



Abstract

This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.

Citation

Gkikas, D. C., Theodoridis, P. K., Theodoridis, T., & Gkikas, M. C. (in press). Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification. Informatics, 10(3), 63. https://doi.org/10.3390/informatics10030063

Journal Article Type Article
Acceptance Date Jul 3, 2023
Online Publication Date Jul 21, 2023
Deposit Date Aug 15, 2023
Publicly Available Date Aug 15, 2023
Journal Informatics
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 10
Issue 3
Pages 63
DOI https://doi.org/10.3390/informatics10030063
Keywords Computer Networks and Communications, Human-Computer Interaction, Communication

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