Novas Medidas de Relevância para Seleção Lazy de Atributos

Douglas B. PereiraAlexandre PlastinoRafael B. PereiraBianca ZadroznyLuiz Henrique de C. MerschmannAlex A. Freitas

Attribute selection is a data preprocessing step used to identify at- tributes relevant to the classification task. Recently, a lazy technique which postpones the choice of attributes to the moment an instance is submitted to classification was proposed. In the original lazy technique proposal, a measure based on the entropy concept was presented to evaluate the quality of the attri- butes. In this work, we propose four new measures, based on: the chi-square statistic test, the Cramer coefficient, the Gini index and the gain ratio concept. Experimental results show the relevance of this proposal since, for a large num- ber of datasets, the best performance of the lazy selection strategy was achieved when the new measures were used.

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