Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Better Prediction of Protein Cellular Localization Sites with the it k Nearest Neighbors Classifier
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
A Comparison of Categorisation Algorithms for Predicting the Cellular Localisation Sites of Proteins
DEXA '01 Proceedings of the 12th International Workshop on Database and Expert Systems Applications
An empirical comparison of supervised machine learning techniques in bioinformatics
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Computers in Biology and Medicine
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The use of artificial intelligence methods in biological data analysis has been increased recent since performance of the classification and detection systems have improved considerably to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm with and without cross validation in a class of imbalanced problems in bioinformatics. Furthermore, we devise an unsupervised AIS algorithm in a supervised manner which contains a training stage for data reduction and a classification stage using fuzzy k-NN algorithm. The experiments show the efficacy of the proposed method with promising results. Using the Escherichia coli and yeast database, we compare the classification accuracy of the proposed method with those of other methods which have been proposed in the literature. The proposed hybrid system produced much more accurate results than the Horton and Nakai's method [P. Horton, K. Nakai, Better prediction of protein cellular localization sites with the k-nearest neighbors classifier, in: Proceedings of Intelligent Systems in Molecular Biology, Halkidiki, Greece, 1997, pp. 368-383]. Besides the improvement on the classification accuracy, one of the important aspects of the proposed method is the complexity. As the proposed AIS method incorporates data reduction in the training stage, the training complexity is considerably low comparing with the k-NN classifier.