Rule Extraction from Binary Neural Networks
نویسندگان
چکیده
A new constructive learning algorithm, called Hamming Clustering (HC), for binary neural networks is proposed. It is able to generate a set of rules in if-then form underlying an unknown classification problem starting from a training set of samples. The performance of HC has been evaluated through a variety of artificial and realworld benchmarks. In particular, its application in the diagnosis of breast cancer has led to the derivation of a reduced set of rules solving the associated classification
منابع مشابه
Knowledge Extraction from the Neural ‘Black Box’ in Ecological Monitoring
Phytoplankton biomass within the Saginaw Bay ecosystem (Lake Huron, Michigan, USA) was characterized as a function of select physical/chemical indicators. The complexity and variability of ecological systems typically make it difficult to model the influences of anthropogenic stressors and/or natural disturbances. Here, Artificial Neural Networks (ANNs) were developed to model chlorophyll a con...
متن کاملRule Extraction from Artiicial Neural Networks Trained on Elementary Number Classiication Tasks
Cascade-Correlation and BpTower networks are trained on pattern classiication tasks. The digits of four-digit integer numbers are sparsley coded and neural networks are trained to recognise the digit patterns of numbers divisible by ve, four and three. The performance of the decompositional rule extraction technique LAP is compared with that of the pedagogical technique RuleVI when extracting r...
متن کاملA Boolean Algebra Based Rule Extraction Algorithm for Neural Networks with Binary or Bipolar Inputs
Neural networks have been applied in various domain including science, commerce, medicine, and industry. However, The knowledge learned by a trained neural network is difficult to understand. This paper proposes a Boolean algebra based algorithm to extract comprehensible Boolean rules from supervised feed-forward neural networks to uncover the black-boxed knowledge. This algorithm is called the...
متن کاملExplanatory Rule Extraction Based on the Trained Neural Network and the Genetic Programming
This paper deals with the use of neural network rule extraction techniques based on the Genetic Programming (GP) to build intelligent and explanatory evaluation systems. Recent development in algorithms that extract rules from trained neural networks enable us to generate classification rules in spite of their intrinsically black-box nature. However, in the original decompositional method looki...
متن کاملINTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...
متن کاملExtracting Propositional Rules from Feed-forward Neural Networks by Means of Binary Decision Diagrams
We discuss how to extract symbolic rules from a given binary threshold feed-forward network. The proposed decompositional approach is based on an internal representation using binary decision diagrams. They allow for an efficient composition of the intermediate results as well as for an easy integration of integrity constraints into the extraction. We also discuss some experimental results indi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1999