DECISION RULES EXTRACTION FROM NEURAL NETWORK: A MODIFIED PEDAGOGICAL APPROACH

Neural network (NN) methods are sometimes useless in practical applications, because they are not pro- perly tailored to the particular market's needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the "Black Box" problem (lack of the NN deci- sions explanatory power). It remains an open issue for the top and middle managerial level. There are though some NN decisions rule extraction methods like decompositional, pedagogical or eclectic, but they suffer from low portability of the rule extraction technique across various neural net architectures, high level of granularity, algorithmic sophisti- cation of the rule extraction technique etc. The author propose to eliminate some known drawbacks using an innovative extension of the pedagogical approach. The idea is exposed by the use of a widespread MLP neural net (as a common tool for the decisions' space fragmentation in the financial problems' domain) and SOM (for the input data space clus- terization). The feedback of both nets' performance is related and targeted through the iteration cycle by achievement of the best matching between the decision space fragments and input data space clusters. Three sets of rules are generated algorithmically or by fuzzy membership functions. Empirical validation of the common financial benchmark problems is conducted with an appropriately prepared software solution.

[1]  Wlodzislaw Duch,et al.  A new methodology of extraction, optimization and application of crisp and fuzzy logical rules , 2001, IEEE Trans. Neural Networks.

[2]  Z. Dong,et al.  Lessons from the past , 2005, Nature.

[3]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[4]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

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

[7]  Joachim Diederich,et al.  The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks , 1998, IEEE Trans. Neural Networks.

[8]  Amir B. Geva,et al.  Hierarchical unsupervised fuzzy clustering , 1999, IEEE Trans. Fuzzy Syst..

[9]  Wee Kheng Leow,et al.  On mapping decision trees and neural networks , 1999, Knowl. Based Syst..

[10]  Bart BaesensRudy.  Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation , 2003 .

[11]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[12]  Jude W. Shavlik,et al.  Using Sampling and Queries to Extract Rules from Trained Neural Networks , 1994, ICML.

[13]  Ah Chung Tsoi,et al.  Rule inference for financial prediction using recurrent neural networks , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[14]  Darius Plikynas,et al.  Research of Neural Network Methods for Compound Stock Exchange Indices Analysis , 2002, Informatica.

[15]  Teuvo Kohonen,et al.  Self-Organizing Maps, Second Edition , 1997, Springer Series in Information Sciences.

[16]  Wee Kheng Leow,et al.  Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks , 2000, ICIS.