One-Class Convex Hull-Based Algorithm for Classification in Distributed Environments

In this paper, a new one-class classification algorithm capable of working in distributed environments is presented. In it, convex hull is used to build the boundary of the target class defining the one-class problem in each of the distributed nodes. Therefore, we will consider several classifiers, each one determined using a given local data partition, and the goal is to obtain a global classification decision. In order to obtain this final decision, two different algebraic combination rules were proposed: 1) sum and 2) majority voting. Experimental results show that this method opens the possibility of tackling practical one-class classification problems in distributed big data scenarios in an efficient and accurate way.

[1]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[2]  Michael Ian Shamos,et al.  Computational geometry: an introduction , 1985 .

[3]  Miriam A. M. Capretz,et al.  Contextual Anomaly Detection in Big Sensor Data , 2014, 2014 IEEE International Congress on Big Data.

[4]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[5]  Yong Zhong,et al.  Anomaly Detection from Distributed Flight Record Data for Aircraft Health Management , 2010, 2010 International Conference on Computational and Information Sciences.

[6]  Verónica Bolón-Canedo,et al.  Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset , 2011, Expert Syst. Appl..

[7]  Paul R. Detmer,et al.  Centroid of a Polygon , 1994, Graphics Gems.

[8]  David A. Clifton,et al.  A review of novelty detection , 2014, Signal Process..

[9]  Dinesh Manocha,et al.  GPU accelerated convex hull computation , 2012, Comput. Graph..

[10]  Shehroz S. Khan,et al.  A Survey of Recent Trends in One Class Classification , 2009, AICS.

[11]  Amparo Alonso-Betanzos,et al.  One-class classification algorithm based on convex hull , 2016, ESANN.

[12]  Valeriy Vyatkin,et al.  Time-Complemented Event-Driven Architecture for Distributed Automation Systems , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Deniz Erdoğmuş,et al.  Multivariate density estimation with optimal marginal parzen density estimation and gaussianization , 2004, Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004..

[14]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[15]  Xue Li,et al.  OcVFDT: one-class very fast decision tree for one-class classification of data streams , 2009, SensorKDD '09.

[16]  Radhakrishnan Nagarajan,et al.  Selective voting in convex-hull ensembles improves classification accuracy , 2012, Artif. Intell. Medicine.

[17]  Ran Wolff,et al.  Noname manuscript No. (will be inserted by the editor) In-Network Outlier Detection in Wireless Sensor Networks , 2022 .

[18]  Shehroz S. Khan,et al.  One-class classification: taxonomy of study and review of techniques , 2013, The Knowledge Engineering Review.

[19]  Enrique F. Castillo,et al.  Distributed One-Class Support Vector Machine , 2015, Int. J. Neural Syst..

[20]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[21]  Bernard Toursel,et al.  Distributed Data Mining , 2001, Scalable Comput. Pract. Exp..

[22]  Derong Liu,et al.  Neural-Network-Based Distributed Adaptive Robust Control for a Class of Nonlinear Multiagent Systems With Time Delays and External Noises , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[23]  Ammar Belatreche,et al.  An experimental evaluation of novelty detection methods , 2014, Neurocomputing.

[24]  Petia Radeva,et al.  Approximate Convex Hulls Family for One-Class Classification , 2011, MCS.

[25]  Fabio Roli,et al.  Intrusion detection in computer networks by a modular ensemble of one-class classifiers , 2008, Inf. Fusion.

[26]  Tong Zhang,et al.  Fall Detection by Wearable Sensor and One-Class SVM Algorithm , 2006 .

[27]  Petia Radeva,et al.  Approximate polytope ensemble for one-class classification , 2014, Pattern Recognit..

[28]  Ming Zeng,et al.  Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings , 2016 .

[29]  Hyun Joon Shin,et al.  One-class support vector machines - an application in machine fault detection and classification , 2005, Comput. Ind. Eng..

[30]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[31]  António E. Ruano,et al.  A Randomized Approximation Convex Hull Algorithm for High Dimensions , 2015 .

[32]  Thomas I. Strasser,et al.  Guest Editorial: Special issue on industrial applications of distributed intelligent systems , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[33]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[34]  Malik Yousef,et al.  One-class document classification via Neural Networks , 2007, Neurocomputing.

[35]  Brian Litt,et al.  One-Class Novelty Detection for Seizure Analysis from Intracranial EEG , 2006, J. Mach. Learn. Res..

[36]  Amparo Alonso-Betanzos,et al.  Power wind mill fault detection via one-class ν-SVM vibration signal analysis , 2011, The 2011 International Joint Conference on Neural Networks.

[37]  Guoyou Wang,et al.  A Novel Geometric Approach to Binary Classification Based on Scaled Convex Hulls , 2009, IEEE Transactions on Neural Networks.

[38]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).