On Combining Instance Selection and Discretisation: A Comparative Study of Two Combination Orders

Kuen-Liang Sue, Chih-Fong Tsai and Tzu-Ming Yan
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Kuen-Liang Sue: Department of Information Management, National Central University, Taoyuan, Taiwan
Chih-Fong Tsai: Department of Information Management, National Central University, Taoyuan, Taiwan
Tzu-Ming Yan: Department of Information Management, National Central University, Taoyuan, Taiwan

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 05, 1-16

Abstract: Data discretisation focuses on converting continuous attribute values to discrete ones which are closer to a knowledge-level representation that is easier to understand, use, and explain than continuous values. On the other hand, instance selection aims at filtering out noisy or unrepresentative data samples from a given training dataset before constructing a learning model. In practice, some domain datasets may require processing with both discretisation and instance selection at the same time. In such cases, the order in which discretisation and instance selection are combined will result in differences in the processed datasets. For example, discretisation can be performed first based on the original dataset, after which the instance selection algorithm is used to evaluate the discrete type of data for selection, whereas the alternative is to perform instance selection first based on the continuous type of data, then using the discretiser to transfer the attribute type of values of a reduced dataset. However, this issue has not been investigated before. The aim of this paper is to compare the performance of a classifier trained and tested over datasets processed by these combination orders. Specifically, the minimum description length principle (MDLP) and ChiMerge are used for discretisation, and IB3, DROP3 and GA for instance selection. The experimental results obtained using ten different domain datasets show that executing instance selection first and discretisation second perform the best, which can be used as the guideline for the datasets that require performing both steps. In particular, combining DROP3 and MDLP can provide classification accuracy of 0.85 and AUC of 0.8, which can be regarded as the representative baseline for future related researches.

Keywords: Data discretisation; instance selection; outliers; data mining; machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500813

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