Memory-based classification with dynamic feature selection using self-organizing maps for pattern evaluation
Memory-based learning is one of the main fields of machine learning. We Propose a new methodology for addressing the classification task that relies on the main idea of the k-nearest neighbors algorithm, which is the most important representative of this field. In the proposed approach, given an unclassified pattern, a set of neighboring patterns is found, not necessarily using all input feature dimensions. Also, following the concept of the naive Bayesian classifier, we adopt the assumption of independence of input features in the outcome of the classification task. The two concepts are merged in an attempt to take advantage of their good performance features. In order to further improve the performance of our approach, we propose a novel weighting scheme of the memory-base. Using the self-organizing maps model during the execution of the algorithm, dynamic weights of the memory-base patterns are produced. Experimental results have shown improved performance of the proposed method in comparison with the aforementioned algorithms and their variations.
classification self-organizing maps memory-based learning feature weighting hybrid systems