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Soft Computing

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02.08.2017 | Methodologies and Application

Temporal Sleuth Machine with decision tree for temporal classification

verfasst von: Shih Yin Ooi, Shing Chiang Tan, Wooi Ping Cheah

Erschienen in: Soft Computing | Ausgabe 24/2018

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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.

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Titel: Temporal Sleuth Machine with decision tree for temporal classification
verfasst von: Shih Yin Ooi
Shing Chiang Tan
Wooi Ping Cheah
Publikationsdatum: 02.08.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 24/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2747-8

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