Temporal Sleuth Machine with decision tree for temporal classification

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.

[1]  J. Ross Quinlan,et al.  Unknown Attribute Values in Induction , 1989, ML.

[2]  Thomas Torsney-Weir,et al.  A fuzzy cognitive map of the psychosocial determinants of obesity , 2012, Appl. Soft Comput..

[3]  Shing Chiang Tan,et al.  Anomaly Based Intrusion Detection through Temporal Classification , 2014, ICONIP.

[4]  Qiang Ji,et al.  Context augmented Dynamic Bayesian Networks for event recognition , 2014, Pattern Recognit. Lett..

[5]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[6]  K. Jeong,et al.  Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics , 2008 .

[7]  Marzieh Zare,et al.  Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine , 2016, Clinical Neurophysiology.

[8]  Vincent S. Tseng,et al.  Effective temporal data classification by integrating sequential pattern mining and probabilistic induction , 2009, Expert Syst. Appl..

[9]  Jose L. Salmeron,et al.  Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series , 2014, Int. J. Approx. Reason..

[10]  Jose L. Salmeron,et al.  Fuzzy grey cognitive maps and nonlinear Hebbian learning in process control , 2013, Applied Intelligence.

[11]  George C. Runger,et al.  Bias of Importance Measures for Multi-valued Attributes and Solutions , 2011, ICANN.

[12]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[13]  Mohammed J. Zaki,et al.  Mining features for sequence classification , 1999, KDD '99.

[14]  Philip S. Yu,et al.  Scoring the Data Using Association Rules , 2003, Applied Intelligence.

[15]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[16]  Raúl Alcaraz,et al.  Dynamic time warping applied to estimate atrial fibrillation temporal organization from the surface electrocardiogram. , 2013, Medical engineering & physics.

[17]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[18]  Davide Anguita,et al.  Energy Efficient Smartphone-Based Activity Recognition Using Fixed-Point Arithmetic , 2013 .

[19]  Gin-Der Wu,et al.  An enhanced discriminability recurrent fuzzy neural network for temporal classification problems , 2014, Fuzzy Sets Syst..

[20]  Jose L. Salmeron,et al.  A Review of Fuzzy Cognitive Maps Research During the Last Decade , 2013, IEEE Transactions on Fuzzy Systems.

[21]  Olaf P Jensen,et al.  Fuzzy cognitive mapping in support of integrated ecosystem assessments: Developing a shared conceptual model among stakeholders. , 2016, Journal of environmental management.

[22]  Elpida T. Keravnou,et al.  Temporal abstraction and temporal Bayesian networks in clinical domains: A survey , 2014, Artif. Intell. Medicine.

[23]  Billur Barshan,et al.  Comparative study on classifying human activities with miniature inertial and magnetic sensors , 2010, Pattern Recognit..

[24]  Derya Avci,et al.  An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange , 2010, Expert Syst. Appl..

[25]  Mohammed Waleed Kadous,et al.  Temporal classification: extending the classification paradigm to multivariate time series , 2002 .

[26]  Mohan Kumar,et al.  Using dynamic time warping for online temporal fusion in multisensor systems , 2008, Inf. Fusion.

[27]  Churn-Jung Liau,et al.  Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree , 2012 .

[28]  Daniele P. Radicioni,et al.  BREVE: An HMPerceptron-Based Chord Recognition System , 2010, Advances in Music Information Retrieval.

[29]  Rubén San-Segundo-Hernández,et al.  Segmenting human activities based on HMMs using smartphone inertial sensors , 2016, Pervasive Mob. Comput..

[30]  Nikola K. Kasabov,et al.  Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes , 2015, Inf. Sci..

[31]  Pablo Hernandez-Leal,et al.  Learning temporal nodes Bayesian networks , 2013, Int. J. Approx. Reason..

[32]  Anninou P. Antigoni,et al.  A New Mathematical Modelling Approach for Viticulture and Winemaking Using Fuzzy Cognitive Maps , 2015 .

[33]  Naveen K. Bansal,et al.  Modeling Temporal Pattern and Event Detection using Hidden Markov Model with Application to a Sludge Bulking Data , 2012, Complex Adaptive Systems.

[34]  Peter Z. Revesz,et al.  Temporal Data Classification Using Linear Classifiers , 2009, ADBIS.

[35]  Rita Durão,et al.  Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models , 2016 .

[36]  Chrysostomos D. Stylios,et al.  Modeling complex systems using fuzzy cognitive maps , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[37]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[38]  Petia Radeva,et al.  Personalization and user verification in wearable systems using biometric walking patterns , 2011, Personal and Ubiquitous Computing.

[39]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[40]  K. Bódis,et al.  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 , 2016 .

[41]  Cheol Oh,et al.  Improving strategic policies for pedestrian safety enhancement using classification tree modeling , 2016 .

[42]  Ali A. Minai,et al.  Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction , 2015, Appl. Soft Comput..

[43]  Witold Pedrycz,et al.  Fuzzy rule based decision trees , 2015, Pattern Recognit..

[44]  Shing Chiang Tan,et al.  Temporal Decision Tree and Interpretable Temporal Rules: J48 and Fuzzy Cognitive Maps Approach , 2014, Aust. J. Intell. Inf. Process. Syst..

[45]  Vincent S. Tseng,et al.  CBS: A New Classification Method by Using Sequential Patterns , 2005, SDM.