Citation

Ooi, Shih Yin and Tan, Shing Chiang and Cheah, Wooi Ping (2017) Temporal sampling forest (TS-F): an ensemble temporal learner. Soft Computing: Methodologies and Applications, 21 (23). pp. 7039-7052. ISSN 1615-3871

Text 10.1007%2Fs00500-016-2242-7.pdf Restricted to Repository staff only Download (2MB)

Abstract

Ensemble learning is in favour of machine learning community due to its tolerance in handling divergence and biasness issues faced by a single learner. In this work, an ensemble temporal learner, namely temporal sampling forest (TS-F), is proposed. Building on the random forest, we consider its limitations in handling temporal classification tasks. Temporal data classification is an important area of machine learning and data mining, where it fills the gap of ordinary data classification when the observed datasets are temporally related across sequential and time domains. TS-F incorporated the temporal sampling (bagging) and temporal randomization procedures in the classical random forest, hence extending its ability to handle temporal data . TS-F was tested on 11 public sequential and temporal datasets from different domains . Experiments demonstrate that TS-F could provide promising results with average classification accuracy of 98 %, substantiating its ability to escalate the random forest performance in the application of temporal classification.

Item Type:	Article
Uncontrolled Keywords:	Soft computing, Ensemble learner, Temporal classification, Random forest, Temporal application
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	10 Jul 2018 12:00
Last Modified:	10 Jul 2018 12:00
URII:	http://shdl.mmu.edu.my/id/eprint/6702

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