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We propose a novel computational granular unified framework that is cognitively motivated for learning if-then fuzzy weighted rules by using a hybrid fuzzy-neuro possibilistic model appropriately crafted as a learning device of fuzzy rules from only raw input-output examples by integrating some useful concepts from the human cognition processes and adding some interesting granular functionalities...
Neural network is a widely used and an effective artificial intelligence technique used for predictions and classifications which has been developed based on human biological neural system. Determining the structure of a neural network is a very complex task and there is no defined approach to determine the structure, especially the number of hidden nodes. Traditionally the number of hidden nodes...
This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular...
Learning classifier systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. The new model of LCS - so-called rGCS - is used to classify real-valued data. In order to handle...
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