论文信息 - Extraction of crisp logical rules from medical datasets

Extraction of crisp logical rules from medical datasets

Three neural-based methods for extraction of logical rules from data are presented. These methods facilitate conversion of graded response neural networks into networks performing logical functions. MLP2LN method tries to convert a standard MLP into a network performing logical operations. C-MLP2LN is a constructive algorithm creating such MLP networks. Logical interpretation is assured by adding constraints to the cost function, forcing the weights to±1 or 0. Skeletal networks emerge ensuring that a minimal number of logical rules are found. In both methods rules covering many training examples are generated before more specific rules covering exceptions. The third method, FSM2LN, is based on the probability density estimation. Results of application of these methods to the hypothyroid and hepar datasets are presented.

[1] Michael R. Berthold,et al. Building precise classifiers with automatic rule extraction , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[2] Wolfram Schiffmann,et al. Comparison of optimized backpropagation algorithms , 1993, ESANN.

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

[4] Sholom M. Weiss,et al. An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.

[5] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[6] Chuen-Tsai Sun,et al. Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.