Temporal Sleuth Machine with decision tree for temporal classification
- 101 Downloads
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.
- Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2013) Energy efficient smartphone-based activity recognition using fixed-point arithmetic. Special session in ambient assisted living: home care. J Univ Comput Sci 19(9):1295–1314Google Scholar
- Antunes CM, Oliveira AL (2001) Temporal data mining: an overview. In: KDD workshop on temporal data mining, pp 1–13Google Scholar
- Basse RM, Charif O, Bódis K (2016) Spatial and temporal dimensions of land use change in cross border region of Luxembourg. Development of a hybrid approach integrating GIS, cellular automata and decision learning tree models. Appl Geogr 67:94–108. doi: 10.1016/j.apgeog.2015.12.001 CrossRefGoogle Scholar
- Deng H, Runger G, Tuv E (2011) Bias of importance measures for multi-valued attributes and solutions. In: Proceedings of the 21st international conference on artificial neural networks (ICANN2011), LNCS 6792, vol 2, pp 293–300Google Scholar
- Kadous M (2002) Temporal classification: extending the classification paradigm to multivariate time series. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Temporal+Classification+:+Extending+the+Classification+Paradigm+to+Multivariate+Time+Series#0
- Karimi K, Hamilton HJ (2001) Temporal rules and temporal rules and temporal decision trees: a C4.5 approach. Technical Report CS-2001-02. Retrieved from https://pdfs.semanticscholar.org/872/88d6cf1c84dc819219d647bdc5708dc53248.pdf
- Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International joint conference on artificial intelligence (IJCAI), vol 5, pp 1137–1143. Morgan Kaufmann, San MateoGoogle Scholar
- Lesh N, Zaki MJ, Ogihara M (1999) Mining features for sequence classification. In: KDD ’99 proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, pp 342–346Google Scholar
- Ooi SY, Tan SC, Cheah WP (2014a) LNCS 8836—anomaly based intrusion detection through temporal classification. Lecture notes in computer science (LNCS), 21st international conference on neural information processing (ICONIP 2014), pp 612–619Google Scholar
- Ooi SY, Tan SC, Cheah WP (2014b) Temporal decision tree and interpretable temporal rules: J48 and fuzzy cognitive maps approach. Aust J Intell Inf Process Syst 14(1). Retrieved from http://cs.anu.edu.au/ojs/index.php/ajiips
- Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
- Radicioni DP, Esposito R (2010) BREVE: an HMPerceptron-based chord recognition system. Adv Music Inf Retr Stud Comput Intell 274:143–164Google Scholar