![]() ![]() The resample set resulting from the previous module, will be used to train CBoost, which will be used to predict bankruptcy for the validation set. This algorithm will perform several iterations for finding weak classifiers and combining them to create a strong classifier. The distance from each sample to its closest centroid will be used to initialize its weight. In this algorithm, the majority class will be clustered into a number of clusters. Then, this study proposes a Cluster-based Boosting algorithm, namely CBoost, for dealing with the class imbalance. ![]() This framework first resamples the imbalance dataset by the undersampling method using Instance Hardness Threshold (IHT), which is used to remove the noise instances having large IHT value in the majority class. Therefore, this study proposes a cluster-based boosting algorithm as well as a robust framework using the CBoost algorithm and Instance Hardness Threshold (RFCI) for effective bankruptcy prediction of a financial dataset. ![]() In a bankruptcy dataset, the problem of class imbalance, in which the number of bankruptcy companies is smaller than the number of normal companies, leads to a standard classification algorithm that does not work well. Bankruptcy prediction has been a popular and challenging research topic in both computer science and economics due to its importance to financial institutions, fund managers, lenders, governments, as well as economic stakeholders in recent years. ![]()
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