Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms

Ali Öztürk

Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms

Abstract

There are two main approaches for feature subset selection, i.e., wrapper and filter based. In wrapper based approach, which is a supervised method, the feature subset selection algorithm acts as a wrapper around an induction algorithm. The induction algorithm is actually a black-box for the feature subset selection algorithm and is mostly the classifier itself. The filter approach is an unsupervised method and attempts to assess the merits of features from the data while ignoring the performance of the induction algorithm. In this study, the effects of the feature subset selection approaches on the classification performance of rule-based learning algorithms, i.e., C4.5, RIPPER, PART, BFTree were investigated. These algorithms are fast in case of wrapper based approach. For various datasets, significant accuracy improvements were achieved with the wrapper based feature subset selection method. Other algorithms like Multilayer Perceptron (MLP) and Random Forests (RF) were also applied on the same datasets for the purpose of accuracy comparison. These two algorithms were very inefficient in terms of time when they were used in wrapper approach.

Keywords

Rule-based learning, feature extraction, wrapper, filtering

References

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Details

Primary Language

English

Subjects

Data Mining and Knowledge Discovery

Journal Section

Research Article

Authors

Ali Öztürk
Türkiye

Publication Date

December 27, 2018

Submission Date

November 27, 2018

Acceptance Date

December 21, 2018

Published in Issue

Year 2018 Volume: 1 Number: 1

IZ

https://izlik.org/JA82UP86DR

APA

Öztürk, A. (2018). Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms. Data Science and Applications, 1(1), 16-20. https://izlik.org/JA82UP86DR

AMA

1.Öztürk A. Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms. DataSCI. 2018;1(1):16-20. https://izlik.org/JA82UP86DR

Chicago

Öztürk, Ali. 2018. “Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms”. Data Science and Applications 1 (1): 16-20. https://izlik.org/JA82UP86DR.

EndNote

Öztürk A (December 1, 2018) Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms. Data Science and Applications 1 1 16–20.

IEEE

[1]A. Öztürk, “Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms”, DataSCI, vol. 1, no. 1, pp. 16–20, Dec. 2018, [Online]. Available: https://izlik.org/JA82UP86DR

ISNAD

Öztürk, Ali. “Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms”. Data Science and Applications 1/1 (December 1, 2018): 16-20. https://izlik.org/JA82UP86DR.

JAMA

1.Öztürk A. Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms. DataSCI. 2018;1:16–20.

MLA

Öztürk, Ali. “Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms”. Data Science and Applications, vol. 1, no. 1, Dec. 2018, pp. 16-20, https://izlik.org/JA82UP86DR.

Vancouver

1.Ali Öztürk. Performance Evaluation of Feature Subset Selection Approaches on Rule-Based Learning Algorithms. DataSCI [Internet]. 2018 Dec. 1;1(1):16-20. Available from: https://izlik.org/JA82UP86DR