Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
Self-organizing maps
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Classification by Voting Feature Intervals
ECML '97 Proceedings of the 9th European Conference on Machine Learning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Proportional k-Interval Discretization for Naive-Bayes Classifiers
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A Bayesian approach to use emerging patterns for classification
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Combining a self-organising map with memory-based learning
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Independent Nearest Features Memory-Based Classifier
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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Memory base learning is one of main fields in the area 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, but not necessarily using all input feature dimensions. Also, following the concept of the naïve Bayesian classifier, we adopt the hypothesis of the 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 superior performance of the proposed method in comparison with the aforementioned algorithms and their variations.