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Abstract. The purpose of this paper is to discuss about feature selection methods. We present two common feature selection approaches: statistical methods and artificial intelligence approach. Statistical methods are exposed as antecedents of classification methods with specific techniques for choice of variables because we pretend to try the feature selection techniques in classification problems. We show the artificial intelligence approaches from different points of view. We also present the use of the information theory to build decision trees. Instead of using Quinlan's Gain we discuss others alternatives to build decision trees. We introduce two new feature selection measures: MLRelevance formula and the PRelevance. These criteria maximize the heterogeneity among elements that belong to different classes and the homogeneity among elements that belong to the same class. Finally, we compare different feature selection methods by means of the classification of two medical data sets.

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This work discusses an important issue in the area of human resource management by proposing a novel model for creation and evaluation of software teams. The model consists of several assessments, including a technical test, a quality of life test and a psychological-sociological test. Since the technical test requires particular organizational specifications and cannot be examined without reference to a specific company, only the sociological test and the quality of life tests are extensively discussed in this work. Two strategies are discussed for assigning roles in a project. Initially, six software projects were selected, and after extensive analysis of the projects, two projects were chosen and correctives actions were applied. An empirical evaluation was also conducted to assess the model's effectiveness. The experimental results demonstrate that the application of the model improved the productivity of project teams.