Panel of Attribute Selection Methods to Rank Features Drastically Improves Accuracy in Filtering Web-pages Suitable for Education
Vladimir Estivill‐Castro, Matteo Lombardi, Alessandro Marani · 2019
Search engines and recommender system take advantage of user queries, characteristics, preferences or perceived needs for filtering results. In contexts such as education, considering the purpose of a resource is also fundamental. A document not suitable for learning, although well related to the query, should never be recommended to a student. However, users are currently obliged to spend additional time and effort for matching the machine-filtered results to their purpose. This paper presents a method for automatically filtering web-pages according to their educational usefulness. Our ground truth is a dataset where items are web-pages classified as relevant for education or not. Then, we present a new feature selection method for lowering the number of attributes of the items. We build a committee of feature selection methods, but do not use it as an ensemble. A comprehensive evaluation of our approach against current practices in feature selection and feature reduction demonstrates that our proposal 1) enables state-of-the-art classifiers to perform a significantly faster, yet very accurate, automatic filtering of educational resources, and 2) such filtering meaningfully considers the usefulness of the resource for educational tasks.