Laura Maria Cannas

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Evolutionary algorithms have received much attention in extracting knowledge on high-dimensional micro-array data, being crucial to their success a suitable definition of the search space of the potential solutions. In this paper, we present an evolutionary approach for selecting informative genes (features) to predict and diagnose cancer. We propose a(More)
Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques(More)
Methods currently used for micro-array data classification aim to select a minimum subset of features, namely a predictor, that is necessary to construct a classifier of best accuracy. Although effective, they lack in facing the primary goal of domain experts that are interested in detecting different groups of biologically relevant markers. In this paper,(More)
In the last ten years, automatic Text Categorization (TC) has been gaining an increasing interest from the research community, due to the need to organize a massive number of digital documents. Following a machine learning paradigm, this paper presents a model which regards TC as a classification task supported by a wrapper approach and combines the(More)
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