A novel classification method using the combination of FDPS and flexible neural tree

The combination of Further Division of Partition Space (FDPS) and Flexible Neural Tree (FNT) is proposed to improve the neural network classification performance. FDPS, which divides partition space into many partitions that will attach to different classes automatically, is a novel technique for neural network classification. FNT is a neural network's structure which uses flexible tree model. The proposed method combines FDPS and FNT to overcome their respective problems by using the other's merit. In order to evaluate the performance of this method, four well-known data sets are used for classification test. Experiment results have shown that this method has favorable performance.

[1]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1995, COLT '90.

[2]  Ravi Jain,et al.  A Comparative Study of Fuzzy Classifiers on Breast Cancer Data , 2009, IWANN.

[3]  Rafal Salustowicz,et al.  Probabilistic Incremental Program Evolution , 1997, Evolutionary Computation.

[4]  Bo Yang,et al.  A Novel Improvement of Neural Network Classification Using Further Division of Partition Space , 2007, IWINAC.

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  Yianni Attikiouzel,et al.  Artificial Neural Networks and Breast Cancer Prognosis , 1994, Aust. Comput. J..

[7]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Bo Yang,et al.  Feature selection and classification using flexible neural tree , 2006, Neurocomputing.

[10]  Hongjun Lu,et al.  Effective Data Mining Using Neural Networks , 1996, IEEE Trans. Knowl. Data Eng..

[11]  Zhi-Hua Zhou,et al.  NeC4.5: Neural Ensemble Based C4.5 , 2004, IEEE Trans. Knowl. Data Eng..

[12]  Zhi-Hua Zhou,et al.  Editing Training Data for kNN Classifiers with Neural Network Ensemble , 2004, ISNN.

[13]  Jiwen Dong,et al.  Time-series forecasting using flexible neural tree model , 2005, Inf. Sci..

[14]  Yuehjen E. Shao,et al.  Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines , 2004, Expert Syst. Appl..

[15]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.