Temporal Sleuth Machine with decision tree for temporal classification

Shih Yin Ooi, Shing Chiang Tan, Wooi Ping Cheah

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)8077-8095
Number of pages19
JournalSoft Computing
Issue number24
Publication statusPublished - 1 Dec 2018
Externally publishedYes


  • C4.5
  • Hybrid model
  • Temporal data classification
  • Temporal decision tree

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology


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