Paper Info
Title
Temporal Sleuth Machine with decision tree for temporal classification.
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.
Year
DOI
Venue
2018
10.1007/s00500-017-2747-8
Soft Comput.
Keywords
Field
DocType
C4.5, Temporal decision tree, Temporal data classification, Hybrid model
Time domain,Decision tree,Data mining,Computer science,Fuzzy cognitive map,Robustness (computer science),Temporal database,Artificial intelligence,Data classification,Information gain ratio,Machine learning
Journal
Volume
Issue
ISSN
22
24
1432-7643
Citations 
PageRank 
References 
0
0.34
22
Authors
3
Name
Order
Citations
PageRank
Shih Yin Ooi1236.80
Shing Chiang Tan212218.99
Wooi Ping Cheah3368.03