Integration of Intelligent Information Technologies Ensembles for Modeling and Classification

Intelligent information technologies help us to solve complex data mining problems and therefore they are of particular interest. However, a generation of a specific technology structure demands high skills of a developer and this process is time-consuming as well. In this paper, we present an automated integration of intelligent information technologies for complex systems modeling and classification. We consider such popular techniques as neural networks, fuzzy rules based systems and neuro-fuzzy systems as well as evolutionary algorithms for automated generation. We also propose a new idea of genetic programming application to the design of intelligent information technologies ensembles for effectiveness and reliability improvement.

[1]  Krzysztof Siwek,et al.  Neural predictor ensemble for accurate forecasting of PM10 pollution , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[2]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[3]  Eugene Semenkin,et al.  Automatized design application of intelligent information technologies for data mining problems , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[4]  Pablo M. Granitto,et al.  A Learning Algorithm For Neural Network Ensembles , 2001, Inteligencia Artif..

[5]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[6]  Giovanna Castellano,et al.  Information granulation via neural network-based learning , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[7]  George D. C. Cavalcanti,et al.  Improving financial time series prediction using exogenous series and neural networks committees , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[8]  Vladimir V. Bukhtoyarov,et al.  Comprehensive evolutionary approach for neural network ensemble automatic design , 2010, IEEE Congress on Evolutionary Computation.

[9]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[10]  Paul C. Rhodes,et al.  Essentials of Fuzzy Modelling and Control , 1995 .

[11]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[12]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[13]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Ronald R. Yager,et al.  Essentials of fuzzy modeling and control , 1994 .

[15]  Emilio Corchado,et al.  Hybrid intelligent algorithms and applications , 2010, Inf. Sci..

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[18]  Giovanna Castellano,et al.  A self-organizing neural fuzzy inference network , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[19]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[20]  Dimitri P. Solomatine,et al.  Multi-model ensemble forecasting in high dimensional chaotic system , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[21]  Oscar Castillo,et al.  Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and Multi Layer Perceptrons combined by a fuzzy inference system , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[22]  Amit Konar,et al.  Computational Intelligence: Principles, Techniques and Applications , 2005 .

[23]  Hisao Ishibuchi,et al.  Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..

[24]  Lars Niklasson,et al.  Building Neural Network Ensembles using Genetic Programming , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[25]  G DietterichThomas.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .

[26]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[27]  John R. Koza,et al.  Genetic programming (videotape): the movie , 1992 .