Webpage Classification with ACO-Enhanced Fuzzy-Rough Feature Selection

Jensen, Richard; Shen, Qiang

doi:10.1007/11908029_17

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…Jensen and Shen [ 37 ] have proposed an ACO enhanced fuzzy rough feature selection for web page classification. Terms extracted from web pages are weighted according to the tf ∗ idf weighting scheme.…”

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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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: 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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“…According to Lee et al [18] where they adopted the graph based ant system to solve feature selection where candidate solution can be represented in directed graphs. Finally, Jensen and Shen [19] represented feature selection as graph where the nodes represent features and the edges between the nodes denote the choice of the next feature. The search for the optimal feature subset is by an ant traversal through the graph where a minimum number of nodes are visited that satisfies the traversal stopping criterion .According Aghdam et al [20] follow similar method like Jensen and Shen but the pheromone is associated with the vertices of graph instead of edges of the graph.…”

Section: Aco For the Solution Of Subset Selection Problemmentioning

confidence: 99%

A Step-by-Step Mathematical Derivation of Ant Colony Optimization for Solving Subset Selection Problems

Mandal

2023

Int. J. Res. Publ. Rev.

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This article presents a thorough, step-by-step mathematical derivation of Ant Colony Optimization (ACO) tailored specifically for solving Subset Selection Problems. ACO, inspired by the foraging behavior of ants, has proven its efficacy as a metaheuristic for combinatorial optimization, and this derivation focuses on its application to subset selection scenarios. Beginning with an exploration of the foundational principles of ACO, the derivation delves into the adaptation of these principles to address subset selection challenges, emphasizing the formulation of the objective function, pheromone updating mechanisms, and solution construction procedures. The mathematical rigor is complemented by intuitive explanations, bridging the theoretical and practical aspects of ACO. Additionally, the article highlights the algorithm's versatility in handling diverse subset selection objectives, showcasing its adaptability to various problem domains. In essence, this comprehensive derivation provides readers with a profound understanding of ACO's inner workings, enabling them to apply and customize the algorithm effectively for subset selection problems in different contexts.

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“…The problem is represented by graph and the ants go on the graph to construct solutions. The solutions are represented by edges in the graph G(V,E) where V is the set of vertices(objects) and E is the set of edges [18][19][20].…”

Section: Modeling and Analysis A Ant System For 0/1 Knapsack Problemmentioning

confidence: 99%

A Step-by-Step Mathematical Derivation of Ant Colony Optimization Algorithm for solving 01 Knapsack Problem

2023

IRJMETS

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This article provides a meticulous step-by-step mathematical derivation of the Ant Colony Optimization (ACO) algorithm designed for solving the 0/1 Knapsack Problem. Each facet of the algorithm, from initialization to pheromone updates, solution construction, and the delicate balance between exploration and exploitation, is dissected. This comprehensive understanding of ACO's underlying mathematical framework offers a clear roadmap for researchers and practitioners, guiding them in harnessing ACO's power to optimize combinatorial challenges. The 0/1 Knapsack Problem, a significant challenge in computer science due to its NP-hard nature, involves strategically loading objects into a knapsack with limited capacity. Despite its computational complexity, this problem finds applications in operations research and finance. The article addresses the urgency for swift solutions by exploring heuristic algorithms inspired by ant behavior, providing a valuable guide for efficiently solving the 0/1 Knapsack Problem.

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Webpage Classification with ACO-Enhanced Fuzzy-Rough Feature Selection

Cited by 12 publications

References 14 publications

An Ant Colony Optimization Based Feature Selection for Web Page Classification

An Ant Colony Optimization Based Feature Selection for Web Page Classification

A Step-by-Step Mathematical Derivation of Ant Colony Optimization for Solving Subset Selection Problems

A Step-by-Step Mathematical Derivation of Ant Colony Optimization Algorithm for solving 01 Knapsack Problem

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