Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021
Adaptive Multi-level Backward Tracking for Sequential Feature Selection

Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021
Adaptive Multi-level Backward Tracking for Sequential Feature Selection

classification accuracy; data mining; dimensionality reduction; sequential feature selection; supervised learning; wrapper approach.

Abstract. In the past few decades, the large amount of available data has become a major challenge in data mining and machine learning. Feature selection is a significant preprocessing step for selecting the most informative features by removing irrelevant and redundant features, especially for large datasets. These selected features play an important role in information searching and enhancing the performance of machine learning models. In this research, we propose a new technique called One-level Forward Multi-level Backward Selection (OFMB). The proposed algorithm consists of two phases. The first phase aims to create preliminarily selected subsets. The second phase provides an improvement on
the previous result by an adaptive multi-level backward searching technique. Hence, the idea is to apply an improvement step during the feature addition and an adaptive search method on the backtracking step. We have tested our algorithm on twelve standard UCI datasets based on k-nearest neighbor and naive Bayes classifiers. Their accuracy was then compared with some popular methods. OFMB showed better results than the other sequential forward searching techniques for most of the tested datasets.

Knitchepon Chotchantarakun & Ohm Sornil

DOI: 10.5614/itbj.ict.res.appl.2021.15.1.1

IRCS-ITB

07 Mei 2021

Sri Wahyuni

ISSN: 2337-5787

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Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 1 2021