Failure prediction of dotcom companies using hybrid intelligent techniques

This paper presents a novel hybrid intelligent system in the framework of soft computing to predict the failure of dotcom companies. The hybrid intelligent system comprises the techniques such as a Multilayer Perceptrons (MLP), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Classification and Regression Trees (CART). The dataset collected from Wharton Research Data Services (WRDS) consists of 240 dotcom companies (also known as click-and-mortar companies), of which 120 are failed and 120 are healthy. Ten-fold cross validation is performed on the data set for all the techniques considered in their stand-alone mode. Further, two hybrid techniques viz., ensembling and boosting are employed to improve the accuracies. Moreover, t-statistic is performed on the dataset for feature selection purpose and the reduced feature subset with 10 features is extracted. The reduced feature subset is tested with all the techniques and then ensembling and boosting is also done for the reduced feature subset. Results supported by Receiver Operating Characteristic (ROC) curve indicate that the important features extracted by the t-statistic based feature subset selection yielded very high accuracies for all the techniques. Furthermore, the results are superior to those reported in previous studies on the same data set.