Temporal Sleuth Machine with decision tree for temporal classification
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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.
KeywordsC4.5 Temporal decision tree Temporal data classification Hybrid model
This research work was supported by two Fundamental Research Grant Schemes (FRGS) under the Ministry of Education and Multimedia University, Malaysia (Project ID: MMUE/130121 and MMUE/160029).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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