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Forecast of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms and governments. Early identification of firms' impending failure is very desirable. The scope of this paper is to investigate the efficiency of multi-instance learning in such an environment. For this reason, a number of experiments have been conducted using representative learning algorithms,...
The main purpose of this study was to determine whether it is possible to somehow use results on training or validation data to estimate ensemble performance on novel data. With the specific setup evaluated; i.e. using ensembles built from a pool of independently trained neural networks and targeting diversity only implicitly, the answer is a resounding no. Experimentation, using 13 UCI datasets,...
Current research at JPL incorporates data mining and machine learning techniques to see whether a better software cost model can be developed. 2CEE is a tool developed for developing new software cost estimation models using data mining techniques. The accuracy of these models has been validated internally through leave-one out cross validation. However, the newly generated models have not been validated...
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression,...
In this correspondence, we point out a discrepancy in a recent paper, "data mining static code attributes to learn defect predictors," that was published in this journal. Because of the small percentage of defective modules, using probability of detection (pd) and probability of false alarm (pf) as accuracy measures may lead to impractical prediction models.
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