Learning Fuzzy Models with a SAX-based Partitioning for Simulated Seizure Recognition

Wearable devices are currently used in researches related with the detection of human activities and the anamnesis of illnesses. Recent studies focused on the detection of simulated epileptic seizures have found that Fuzzy Rule Base Classifiers (FRBC) can be learnt with Ant Colony Systems (ACS) to efficiently deal with this problem. However, the computational requirements for obtaining these models is relatively high, which suggests that an alternative for reducing the learning cost would be rather interesting. Therefore, this study focuses on reducing the complexity of the model by using a discretization technique, more specifically, the discretization proposed in the SAX Time Series (TS) representation.

[1]  Eamonn J. Keogh,et al.  Exact Discovery of Time Series Motifs , 2009, SDM.

[2]  Bart Baesens,et al.  Editorial survey: swarm intelligence for data mining , 2010, Machine Learning.

[3]  C. Panayiotopoulos.  A Clinical Guide to Epileptic Syndromes and Their Treatment, 2nd ed. , 2002, American Journal of Neuroradiology.

[4]  Eamonn J. Keogh,et al.  iSAX: indexing and mining terabyte sized time series , 2008, KDD.

[5]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[7]  Bo Liu,et al.  Density-Based Heuristic for Rule Discovery with Ant-Miner , 2002 .

[8]  José Ramón Villar,et al.  Comparing ACO Approaches in Epilepsy Seizures , 2016, HAIS.

[9]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[10]  María José del Jesús,et al.  Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges , 2015, Knowl. Based Syst..

[11]  Elske Ammenwerth,et al.  Measurement and quantification of generalized tonic–clonic seizures in epilepsy patients by means of accelerometry—An explorative study , 2011, Epilepsy Research.

[12]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[13]  Víctor M. González Suárez,et al.  Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition , 2016, Int. J. Neural Syst..

[14]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[15]  John McGee,et al.  Discretization of Time Series Data , 2005, J. Comput. Biol..

[16]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[17]  Marco Dorigo,et al.  Ant-Based Clustering and Topographic Mapping , 2006, Artificial Life.

[18]  Monique Snoeck,et al.  Classification With Ant Colony Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Paula M. Vergara,et al.  Fuzzy rule learning with ACO in epilepsy crisis identification , 2015, 2015 11th International Conference on Innovations in Information Technology (IIT).

[20]  Jorge Casillas,et al.  Learning Fuzzy Rules Using Ant Colony Optimization Algorithms , 2000 .

[21]  Hussein A. Abbass,et al.  Classification rule discovery with ant colony optimization , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[22]  Alex Alves Freitas,et al.  Data mining with an ant colony optimization algorithm , 2002, IEEE Trans. Evol. Comput..

[23]  Camelia Chira,et al.  Improving Human Activity Recognition and its Application in Early Stroke Diagnosis , 2015, Int. J. Neural Syst..

[24]  Alex A. Freitas,et al.  An Ant Colony Algorithm for Classification Rule Discovery , 2002 .