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Algorithm for the selection of attributes in the prediction of business failure

Abstract:

The selection of relevant attributes is a common task in data mining. In predicting business failure it is useful to have techniques that allow you to select financial reasons that have high predictive power. In this paper an algorithm is proposed that in each iteration selects a subset of attributes through random matrix decomposition. Each subset of attributes is evaluated by its predictive capacity calculated using the technique k closest neighbors with cross validation. The performance of the algorithm is tested with a set of data that contains financial reasons for failed companies and non-failed companies. The results obtained are comparable with those reported in the literature for the same data set.