Double Relief with progressive weighting function

Feature weighting algorithms try to solve a problem of great importance nowadays in machine learning: The search of a relevance measure for the features of a given domain. This relevance is primarily used for feature selection as feature weighting can be seen as a generalization of it, but it is also useful to better understand a problem’s domain or to guide an inductor in its learning process. Relief family of algorithms are proven to be very effective in this task. On previous work, a new extension was proposed that aimed for improving the algorithm’s performance and it was shown that in certain cases it improved the weights’ estimation accuracy. However, it also seemed to be sensible to some characteristics of the data. An improvement of that previously presented extension is presented in this work that aims to make it more robust to problem specific characteristics. An experimental design is proposed to test its performance. Results of the tests prove that it indeed increase the robustness of the previously proposed extension. 1 Overview Feature selection is undoubtedly one of the most important problems in machine learning, pattern recognition and information retrieval, among others. A feature selection algorithm is a computational solution that is motivated by a certain definition of relevance. However, the relevance of a feature may have several definitions depending on the objective that is looked after. On the other hand, feature weighting algorithms try to estimate relevance (in the form of weights to the features) rather than binarily deciding whether a feature is either relevant or not. This is a much harder problem, but also a more flexible framework from an inductive learning perspective. This kind of algorithms are confronted with the down-weighting of irrelevant features, the