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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.

Panel of Attribute Selection Methods to Rank Features Drastically Improves Accuracy in Filtering Web-pages Suitable for Education