Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic

Abstract: This paper describes a novel binary classification method named LASCUS that can be applied to uneven datasets and sensitive problems such as malfunction detection. Such method aims at filling the gap left by traditional algorithms which have difficulties when coping with unbalanced datasets and are not able to satisfactorily recognize unfrequent patterns. The proposed method is based on the use of a self organizing map (SOM) and of a fuzzy inference system (FIS). The SOM creates a set of clusters to be associated either to frequent or unfrequent situations while the FIS determines such association on the basis of data distribution. The method has been tested on the widely used benchmarking Wisconsin breast cancer database and on two industrial applications. The obtained results, which are discussed in the paper, are encouraging and in line with expectations.

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

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

[3]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[4]  J. A. Momoh,et al.  A neural net based approach for fault diagnosis in distribution networks , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[5]  Jean-Claude Paul,et al.  Improved Algebraic Algorithm on Point projection for B´eziercurves , 2007 .

[6]  Robi Polikar,et al.  Ensemble Techniques with Weighted Combination Rules for Early Diagnosis of Alzheimer's Disease , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[7]  Michael R. Berthold,et al.  From radial to rectangular basis functions : A new approach for rule learning from large datasets , 1995 .

[8]  Hussein A. Abbass,et al.  An evolutionary artificial neural networks approach for breast cancer diagnosis , 2002, Artif. Intell. Medicine.

[9]  Rudy Setiono,et al.  Use of a quasi-Newton method in a feedforward neural network construction algorithm , 1995, IEEE Trans. Neural Networks.

[10]  Michael R. Berthold,et al.  Constructing fuzzy graphs from examples , 1999, Intell. Data Anal..

[11]  Taeho Jo,et al.  A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..

[12]  Marta Prim,et al.  Rectangular Basis Functions Applied to Imbalanced Datasets , 2007, ICANN.

[13]  Teuvo Kohonen,et al.  Improved versions of learning vector quantization , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[14]  Bin Yao,et al.  Fault detection for nonlinear systems in presence of input unmodeled dynamics , 2007, 2007 IEEE/ASME international conference on advanced intelligent mechatronics.

[15]  M. Prim,et al.  Adapting Fuzzy Points for Very-Imbalanced Datasets , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[16]  Peng Li,et al.  Hybrid Kernel Machine Ensemble for Imbalanced Data Sets , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[17]  Lijuan Liu,et al.  An Evolutionary Artificial Neural Network Approach for Breast Cancer Diagnosis , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[18]  Chih-Ming Chen,et al.  An efficient fuzzy classifier with feature selection based on fuzzy entropy , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Phayung Meesad,et al.  Combined numerical and linguistic knowledge representation and its application to medical diagnosis , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Automatic detection of left ventricular asynergy by fuzzy reasoning , 2004, Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004..

[21]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[22]  Shlomo Geva,et al.  Rule extraction from local cluster neural nets , 2002, Neurocomputing.

[23]  Chien-Chung Chan,et al.  SVM Approach to Breast Cancer Classification , 2007 .

[24]  Pavel Brazdil,et al.  Proceedings of the European Conference on Machine Learning , 1993 .

[25]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[26]  Stephen Kwek,et al.  Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.

[27]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[28]  J. F. Baldwin.  Fuzzy logic and fuzzy reasoning , 1979 .

[29]  Xiangji Huang,et al.  Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles , 2006, PAKDD.

[30]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[31]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.