Website Quality Analytics Using Metaheuristic Based Optimization

Title:Website Quality Analytics Using Metaheuristic Based Optimization

Volume: 14 Issue: 3

Author(s): Akshi Kumar and Anshika Arora*

Affiliation:

• Department of Computer Science and Engineering, Delhi Technological University, Delhi,India

Keywords: Website quality, feature selection, metaheuristics, classification, e-activities, user experiences.

Abstract:

Background: Studies are indicative of the fact that high-quality websites get better rankings on the search engines. A good website is the one which provides reliable content, has good design and user interface and can address global audience. But the end-users struggle with the predicament of selecting qualitative websites. Although “Quality” is fairly a subjective term, there is an obvious need for a useful and valid model which evaluates the quality attributes of a website. “A Website quality model essentially consists of a set of criteria used to determine if a website reaches certain levels of fineness”.

Objective: The quality of a website must be assured in terms of technicality, the accuracy of information, response time, design of website, ease of use, and many more. The aim is to identify features of a website that determines its quality and build an automatic website quality prediction model.

Methods: We conduct an empirical study on 700 websites and run 6 baseline classifiers to categorize websites into good, average and poor using quality attributes. Subsequently, metaheuristicbased algorithms (Particle Swarm Optimization, Elephant Search Algorithm and Wolf Search Algorithm) for optimal feature selection have been implemented to get an optimal subset of quality attributes that is able to predict the quality of websites more accurately.

Results: The study confirms that the proposed implementation of metaheuristics for feature selection in website quality classification improves the performance of the supervised learning algorithms. An average 12.74% improvement in accuracy was observed using the features selected by Particle Swarm Optimization, 5.56% average improvement in accuracy using Elephant Search Algorithm for feature selection while an average improvement of 5.77% was observed using Wolf Search Algorithm for feature selection.

Conclusion: The study validates that Particle Swarm Optimization for feature selection in website quality analytics task outperforms Wolf Search Algorithm and Elephant Search Algorithm.

Cite this article as:

Kumar Akshi and Arora Anshika *, Website Quality Analytics Using Metaheuristic Based Optimization, Recent Advances in Computer Science and Communications 2021; 14 (3) . https://dx.doi.org/10.2174/2666255813666190911112235