Performance Analysis of Statistical Pattern Recognition Methods in KEEL

Abstract This article represents the focused performance analysis of the statistical Pattern Recognition methods. There is a variety of emerging pattern recognition problems for different data representations like multimedia, spatial, temporal and textual data. Statistical Pattern Recognition approaches are extensively applied for pattern recognition and classification purposes. There are various statistical pattern-recognition methods proposed in the contemporary literature. For the selection of more appropriate statistical pattern recognition, method demands comprehensive performance evaluation of contemporary methods. However, this empirical study is focused on the performance evaluation of statistical methods for pattern discovery in terms of accuracy and error rate. The studied methods include Naive-Bayes (NB-C), Linear Discriminant Analysis (LDA-C), Kernel Classifier (Kernel-C), Least Mean Square Linear Classifier (LinearLMS-C), Least Mean Square Quadratic classifier(PolQuadraticLMS-C), Multinomial logistic regression model with a ridge estimator(Logistic-C) and Particle Swarm Optimization - Linear Discriminant Analysis (PSOLDA-C). The implementation of mentioned methods in KEEL, a data mining tool, is applied on public datasets for the comparative analysis. Experimental results reveal that the performance of PSOLDA-C is promising than other methods in terms of accuracy.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Jesús Alcalá-Fdez,et al.  KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..

[3]  Antoine Geissbühler,et al.  Comparative Performance Analysis of State-of-the-Art Classification Algorithms Applied to Lung Tissue Categorization , 2010, Journal of Digital Imaging.

[4]  Hyeran Byun,et al.  A Survey on Pattern Recognition Applications of Support Vector Machines , 2003, Int. J. Pattern Recognit. Artif. Intell..

[5]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[6]  Shih-Wei Lin,et al.  PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis , 2009, Appl. Soft Comput..

[7]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  B. Eswara Reddy,et al.  Comparative Analysis of Pattern Recognition Methods: An Overview , 2011 .

[9]  Oscar Castillo,et al.  A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition , 2014, Appl. Soft Comput..

[10]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[11]  Alessia Amelio,et al.  Language discrimination by texture analysis of the image corresponding to the text , 2018, Neural Computing and Applications.

[12]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[13]  Basavaraj S. Anami,et al.  Comparative performance analysis of three classifiers for acoustic signal-based recognition of motorcycles using time- and frequency-domain features , 2012 .

[14]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[15]  Arthur Gretton,et al.  Comparison of Pattern Recognition Methods in Classifying High-resolution Bold Signals Obtained at High Magnetic Field in Monkeys , 2008 .

[16]  N. N. Karnik,et al.  Introduction to type-2 fuzzy logic systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[17]  Nihan Kazak.  Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition , 2016 .

[18]  Oana Diana Eva,et al.  Comparison of Classifiers and Statistical Analysis for EEG Signals Used in Brain Computer Interface Motor Task Paradigm , 2015 .

[19]  Harry Wechsler,et al.  Mixture of experts for classification of gender, ethnic origin, and pose of human faces , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[21]  Thomas G. Dietterich,et al.  Locally Adaptive Nearest Neighbor Algorithms , 1993, NIPS.

[22]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[23]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[24]  Shaogang Gong,et al.  Multi-view face detection using support vector machines and eigenspace modelling , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[25]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[26]  M. E. Maron,et al.  Automatic Indexing: An Experimental Inquiry , 1961, JACM.

[27]  George A. Papakostas,et al.  Distance and similarity measures between intuitionistic fuzzy sets: A comparative analysis from a pattern recognition point of view , 2013, Pattern Recognit. Lett..

[28]  Alessia Amelio,et al.  An approach to the language discrimination in different scripts using adjacent local binary pattern , 2017, J. Exp. Theor. Artif. Intell..

[29]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[30]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. I , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[31]  Barath Narayanan Narayanan,et al.  Performance analysis of machine learning and pattern recognition algorithms for Malware classification , 2016, 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS).

[32]  Imtiaz A. Taj,et al.  A comprehensive comparative performance analysis of Laplacianfaces and Eigenfaces for face recognition , 2011 .

[33]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[34]  Manuele Bicego,et al.  A comparative analysis of basic pattern recognition techniques for the development of small size electronic nose , 2002 .

[35]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[36]  Gurpreet Singh Lehal,et al.  Comparative Performance Analysis of Feature(S)-Classifier Combination for Devanagari Optical Character Recognition System , 2014 .