Simultaneous feature selection and ant colony clustering

Akarsu, Emre; Karahoca, Adem

doi:10.1016/j.procs.2011.01.026

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…Several ACO based feature selection algorithms have been proposed for the UCI dataset. The authors of [ 22 – 26 , 28 , 29 , 31 , 33 , 34 ] have reported that their proposed ACO based algorithms increase the performance of classification. According to our experimental results, we also observed that ACO based feature selection algorithm improves the classification performance in terms of classification accuracy and time for the WebKB and Conference datasets in general.…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

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An Ant Colony Optimization Based Feature Selection for Web Page Classification

Saraç

Özel

2014

The Scientific World Journal

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The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

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Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

“…Akarsu and Karahoca [ 29 ] have used ACO for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer dataset.…”

Section: Related Workmentioning

confidence: 99%

An Ant Colony Optimization Based Feature Selection for Web Page Classification

Saraç

Özel

2014

The Scientific World Journal

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“…One is setting the current value of pheromone to the initial value once every 50 iterations while the other is setting the pheromones on all paths to the initial value if the pheromones on the paths remain intact for the past 10 iterations in a row. Akarsu and Karahoca [28] used sequential backward selection (SBS) to reduce the dimensions of patterns. They use Manhattan distance as the fitness function and consider the pheromone only when the ants select paths.…”

Section: Ant Colony Optimization Formentioning

confidence: 99%

“…Then, in the local pheromone update phase, the positive pheromone values will be updated, as lines (18)- (21) show. Finally, in the global pheromone update phase, the best and worst ants will be found and the positive and negative pheromone values on the paths they passed through will be updated, as lines (23)- (28) depict. Once the termination criterion is met, MPTACO will output the best result (line (30)) and terminate the search process.…”

Section: Pseudocode Of Mptacomentioning

confidence: 99%

A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

Tsai

Chiang

et al. 2015

Mathematical Problems in Engineering

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Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

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