Artificial Bee Colony based Data Mining Algorithms for Classification Tasks

Abstract

Artificial Bee Colony (ABC) algorithm is considered new and widely used in searching for optimum solutions. This is due to its uniqueness in problem-solving method where the solution for a problem emerges from intelligent behaviour of honeybee swarms. This paper proposes the use of the ABC algorithm as a new tool for Data Mining particularly in classification tasks. Moreover, the proposed ABC for Data Mining were implemented and tested against six traditional classification algorithms classifiers. From the obtained results, ABC proved to be a suitable candidate for classification tasks. This can be proved in the experimental result where the performance of the proposed ABC algorithm has been tested by doing the experiments using UCI datasets. The results obtained in these experiments indicate that ABC algorithm are competitive, not only with other evolutionary techniques, but also to industry standard algorithms such as PART, SOM, Naive Bayes, Classification Tree and Nearest Neighbour (kNN), and can be successfully applied to more demanding problem domains.

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“…carefully through quantization process to make a compact image with smaller size in terms of bytes [7]. The bits' number from an original image was reduced during the quantization process, in other words the number of colours used in an image was scaled-down so that the image can be view on limited colours display devices.…”

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