Abstract
In this information age many research work are carried out in web page classification to acquire the relevant and appropriate information. To be more specific, for enhancing the web page classification to obtain the optimized feature sets are chosen by utilizing the evolutionary algorithms. Normally, these algorithms are designed by the heuristic principles stimulated by natural evolution. After analyzing the significance of the various evolutionary algorithms deployed by several researchers in this domain so far, this work also intended to apply them to acquire the best solutions (enhanced features). In general, applying the evolutionary algorithms the fittest genes are generated and determined by the fitness function. Once the fittest genes are decided picking up the fittest individual genomes from a population for taking them to the next generations is the challenging task. In this article a novel approach is proposed to choose the best solutions.
Highlights
The classification of web pages can be further improved by the way of effective feature selection approaches
The experimental results of the various evolutionary algorithms utilized for enhancing the feature selection process together with the several university based data sets are summarized with a detailed discussion
The obtained results were compared with the other conventional selection methods such as Threshold Selection (THS), Roulette Wheel Selection (RWS), Tournament selection (TS) and Stochastic Universal Selection (SUS)
Summary
The classification of web pages can be further improved by the way of effective feature selection approaches. This article focuses on effective feature selection for better classification by applying the most common evolutionary algorithms found in the literature [3] [4]. They are: Ant Colony Optimization, Firefly Algorithm, Particle Swarm optimization [5], Cuckoo Search algorithm [6], Bat Algorithms, Wolf Search and Genetic algorithms or programming. The evolutionary algorithms at a certain stage evaluate the choices of feature sets and filter them for further processing. This filtering can be done by several selection methods. By comparing the empirical results of these methods this article proposes a new selection method to acquire the optimum feature sets that enable the web page classification
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