Rule Extraction From Artificial Neural Networks Under Background Knowledge
نویسندگان
چکیده
Labaf, Maryam. M.S, Department of Mathematics and Statistics, Wright State University, 2017. Rule Extraction From Artificial Neural Networks Under Background Knowledge. It is well-known that the input-output behavior of a neural network can be recast in terms of a set of propositional rules, and under certain weak preconditions this is also always possible with positive (or definite) rules. Furthermore, in this case there is in fact a unique minimal (technically, reduced) set of such rules which perfectly captures the inputoutput mapping. In this dissertation, we investigate to what extent these results and corresponding rule extraction algorithms can be lifted to take additional background knowledge into account. It turns out that uniqueness of the solution can then no longer be guaranteed. However, the background knowledge often makes it possible to extract simpler, and thus more easily understandable, rulesets which still perfectly capture the input-output mapping.
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 کاملPropositional Rule Extraction from Neural Networks under Background Knowledge
It is well-known that the input-output behaviour of a neural network can be recast in terms of a set of propositional rules, and under certain weak preconditions this is also always possible with positive (or definite) rules. Furthermore, in this case there is in fact a unique minimal (technically, reduced) set of such rules which perfectly captures the inputoutput mapping. In this paper, we in...
متن کاملClassification of Data to Extract Knowledge from Neural Networks
A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is ...
متن کاملAn Automated Rule Refinement System
Artificial neural networks (ANNs) are essentially a ‘black box’ technology. The lack of an explanation component prevents the full and complete exploitation of this form of machine learning. This chapter presents an historical perspective on rule extraction from artificial neural networks beginning with 6 ways in which the ANN paradigm may be enriched through the addition of rule extraction / e...
متن کاملOptimization of Oleuropein Extraction from Olive Leaves using Artificial Neural Network
In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as amount of flow intensity ratio, temperature, residence time, and pH are used as input variables of the network, whereas the extraction yield is considere...
متن کامل