Knowledge discovery and integration based on a novel neural network ensemble model

This article explores the utility of neural network ensembles in knowledge discovery and integration. A novel neural network ensemble model KBNNE (Knowledge-Based Neural Network Ensembles) integrating KDD (Knowledge Discovery in Database) techniques and neural network modeling algorithms by ¿parallel operations¿ is proposed. Through balancing the relative importance of knowledge learned by induction and deduction, KBNNE can avoid the knowledge loss and enhance the "transparency" of neural network models. The effectiveness of the proposed model is demonstrated through computer simulations on simple artificial problems and an actual modeling problem.

[1]  Tomaso Poggio,et al.  Incorporating prior information in machine learning by creating virtual examples , 1998, Proc. IEEE.

[2]  Yunqian Ma,et al.  Multiple model classification using SVM-based approach , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[3]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[4]  Francine Berman.  Viewpoint: From TeraGrid to knowledge grid , 2001, CACM.

[5]  Hai Zhuge,et al.  China's E-Science Knowledge Grid Environment , 2004, IEEE Intell. Syst..

[6]  Sebastian Thrun,et al.  Explanation-based neural network learning a lifelong learning approach , 1995 .

[7]  Francine Berman.  From TeraGrid to Knowledge Grid. , 2001 .

[8]  Hai Zhuge,et al.  The knowledge grid , 2004 .

[9]  Wu Tao.  Interpolation Based Kernel Function's Construction , 2003 .

[10]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[11]  David A. Bell,et al.  The role of domain knowledge in data mining , 1995, CIKM '95.

[12]  Mario Cannataro,et al.  The knowledge grid , 2003, CACM.

[13]  Robert E. Schapire,et al.  Boosting with prior knowledge for call classification , 2005, IEEE Transactions on Speech and Audio Processing.

[14]  David W. Opitz,et al.  Actively Searching for an E(cid:11)ective Neural-Network Ensemble , 1996 .

[15]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[16]  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..

[17]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[18]  Sankar K. Pal,et al.  Data mining in soft computing framework: a survey , 2002, IEEE Trans. Neural Networks.

[19]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[20]  Tom Heskes,et al.  Clustering ensembles of neural network models , 2003, Neural Networks.

[21]  Tao Wu,et al.  Interpolation based kernel function's constructing , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[22]  F. Rattay,et al.  The handbook of brain theory and neural networks , 1996 .