We present a cluster-based sampling and ensemble method to learn from large, imbalanced data set for bleeding detection in CE videos. Our method selects training examples randomly according to the data distributions derived from clustering. Multiple training sets are created such that data balance is restored. The sampling probability is proportional to the cluster distribution, and within each...

In the software quality modeling process, many practitioners often ignore problems such as high dimensionality and class imbalance that exist in data repositories. They directly use the available set of software metrics to build classification models without regard to the condition of the underlying software measurement data, leading to a decline in prediction performance and extension of train...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

In this paper, we propose a new Random Forest (RF) based ensemble method, ForesTexter, to solve the imbalanced text categorization problems. RF has shown great success in many real-world applications. However, the problem of learning from text data with class imbalance is a relatively new challenge that needs to be addressed. A RF algorithm tends to use a simple random sampling of features in b...

Classifier learning with datasets which suffer from imbalanced class distributions is an important problem in data mining. This issue occurs when the number of examples representing one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. The aim of this work is to shortly review the main i...

Machine Learning algorithms produce accurate classifiers when trained on large, balanced datasets. However, it is generally expensive to acquire labeled data, while unlabeled data is available in much larger amounts. A cost-effective alternative is to use Semi-Supervised Learning, which uses unlabeled data to improve supervised classifiers. Furthermore, for many practical problems, data often e...

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However, they usually ignore positional relationship between a sample and heterogeneous samples its neighborhood when calculating weight. This paper proposes novel neighborhood-weighted method named NWBBagging improve Bagging algorithm's performance imbalance...