Semi-automated data classification with feature weighted self organizing map

This paper presents a Feature Weighted Self-Organizing Map (FWSOM) that analyses the topology information of a converged standard Self organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with irrelevant inputs. We demonstrate an improved classification accuracy with the proposed method by comparison with the standard SOM and other relevant existing classifiers on synthetic and real-world datasets. In addition, the FWSOM method was able to successfully identify the relevant features which in turn were able to improve the classification performance of the other classification methods.

[1]  Aluizio F. R. Araújo,et al.  Dimension Selective Self-Organizing Maps With Time-Varying Structure for Subspace and Projected Clustering , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Nadia Mesghouni,et al.  Unsupervised Double local weighting for feature selection , 2011, 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference.

[3]  Corinna Cortes,et al.  Boosting Decision Trees , 1995, NIPS.

[4]  Mustapha Lebbah,et al.  From variable weighting to cluster characterization in topographic unsupervised learning , 2009, 2009 International Joint Conference on Neural Networks.

[5]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[6]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[7]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[8]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[9]  Renato Cordeiro de Amorim,et al.  Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering , 2012, Pattern Recognit..

[10]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[11]  Zhu Ming-han,et al.  Fisher linear discriminant analysis algorithm based on vector muster , 2011 .

[12]  Tristan Fletcher,et al.  Support Vector Machines Explained , 2008 .

[13]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

[14]  I. W. Evett,et al.  Rule induction in forensic science , 1989 .

[15]  Tiande Guo,et al.  Generalized Graph Regularized Non-negative Matrix Factorization for Data Representation , 2013 .

[16]  Aluizio F. R. Araújo,et al.  Dimension Selective Self-Organizing Maps for clustering high dimensional data , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[17]  Jing Huang,et al.  Automatic Hierarchical Color Image Classification , 2003, EURASIP J. Adv. Signal Process..

[18]  Philip H. Swain,et al.  Purdue e-Pubs , 2022 .

[19]  Edward R. Dougherty,et al.  Performance of feature-selection methods in the classification of high-dimension data , 2009, Pattern Recognit..

[20]  Russ Greiner,et al.  Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals , 2016, Artif. Intell. Res..

[21]  Fernando Jiménez,et al.  Unsupervised feature selection for interpretable classification in behavioral assessment of children , 2017, Expert Syst. J. Knowl. Eng..

[22]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.