A fuzzy logic-based method for outliers detection

The paper presents a method based on a fuzzy inference system that is capable of pointing out outliers in a series of data. The proposed algorithm has been adopted in order to process data coming from a real industrial context. The overall purpose of the work is to point out anomalous data due to several causes such as erroneous measurements, errors made by the operator that filled the database or anomalous process conditions. A standard statistical technique for outlier detection as been exploied for the same purpose: the performance obtained by the two methods are compared and discussed.

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

[2]  F. E. Grubbs.  Procedures for Detecting Outlying Observations in Samples , 1969 .

[3]  Pasi Fränti,et al.  Outlier detection using k-nearest neighbour graph , 2004, ICPR 2004.

[4]  Hongxing He,et al.  A comparative study of RNN for outlier detection in data mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[5]  R. L. Kashyap,et al.  A cluster based approach to robust regression and outlier detection , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[6]  A. Hadi.  A Modification of a Method for the Detection of Outliers in Multivariate Samples , 1994 .

[7]  Guido Smits,et al.  Robust outlier detection using SVM regression , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

[9]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[10]  Ruben H. Zamar,et al.  Robust space transformations for distance-based operations , 2001, KDD '01.

[11]  I. Helland.  Partial least squares regression and statistical models , 1990 .

[12]  J. Bezdek,et al.  Fuzzy partitions and relations; an axiomatic basis for clustering , 1978 .

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[15]  Derek A. Linkens,et al.  Preprocessing of industrial process data with outlier detection and correction , 1999, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296).

[16]  Moshe Kam,et al.  The credibilistic fuzzy c means clustering algorithm , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[17]  Christos Faloutsos,et al.  LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

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

[19]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[20]  Sabine Süsstrunk,et al.  Outlier Modeling in Image Matching , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Erkki Oja,et al.  Principal components, minor components, and linear neural networks , 1992, Neural Networks.