Citation

Ooi, Shih Yin and Cheah, Wooi Ping and Tan, Shing Chiang (2018) Temporal Sleuth Machine with decision tree for temporal classification. Soft Computing, 20 (24). pp. 8077-8095. ISSN 1432-7643

Text Ooi2018_Article_TemporalSleuthMachineWithDecis.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Temporal data classification is an extension field of data classification, where the observed datasets are temporally related across sequential domain and time domain. In this work, an inductive learning temporal data classification, namely Temporal Sleuth Machine (TSM), is proposed. Building on the latest release of C4.5 decision tree (C4.8), we consider its limitations in handling a large number of attributes and inherited information gain ratio problem. Fuzzy cognitive maps is incorporated in the TSM initial learning mechanism to adaptively harness the temporal relations of TSM rules. These extracted temporal values are used to revisit the information gain ratio and revise the number of TSM rules during the second learning mechanism, hence, yielding a stronger learner. Tested on 11 UCI Repository sequential datasets from diverse domains, TSM demonstrates its robustness by achieving an average classification accuracy of more than 95% in all datasets.

Item Type:	Article
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	24 Apr 2021 15:44
Last Modified:	24 Apr 2021 15:44
URII:	http://shdl.mmu.edu.my/id/eprint/7640

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