A Hybrid Classifier with Genetic Weighting
نویسندگان
چکیده
This paper presents the results obtained when classifying a group of artificial and real world data, using a Hybrid Classifier with Genetic Weighting (HCGW). The algorithm proposed is an ensemble based system, it combines several types of classifiers: Naive Bayes, K-Means, k-Nearest Neighbours, C4.5, Decision Tables and ADTree, using a voting criterion for weighted majority to combine the individual classifications of each classifier, assigning the weights for each classifier using a genetic algorithm. We performed tests on data with different tools for Data Mining, like SIPINA, TANAGRA and WEKA, to have a good comparison with the proposed algorithm. Using standard measures such as accuracy, HCGW obtained better performance against different implementations, from those tools, including traditional Ensemble Algorithms.
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