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Bankruptcy Prediction Using Feature Projection Based Classiication

Bankruptcy prediction has been an important decision-making process for nancial analysts. One of the most common approaches for the bankruptcy prediction problem is the Discrim-inant Analysis. Also, the k-Nearest Neighbor classiier is very successful in such domains. This paper proposes a Feature Projection based classiication algorithm, and explores its applicability to the problem of predicting bankruptcy of large rms. The algorithm is evaluated on a particular data set, and its performance is compared with the techniques mentioned above. The experiments indicate that the feature projection based classiication algorithm introduced here performs better than these techniques.