In this paper we propose two ways to deal with the imbalanced data classification problem using random forest. One is based on cost sensitive learning, and the other is based on a sampling technique. Performance metrics such as precision and recall, false positive rate and false negative rate, F-measure and weighted accuracy are computed. Both methods are shown to improve the prediction accurac...

Learning from data with severe class imbalance is difficult. Established solutions include: under-sampling, adjusting classification threshold, and using an ensemble. We examine the performance of combining these solutions to balance the sensitivity and specificity for binary classifications, and to reduce the MSE score for probability estimation.

Class imbalance problems have drawn increasing interest lately because of its classification trouble caused by imbalanced class deliveries and poor prediction performance for minority class. This problem is particularly common in preparation and can be detected in various disciplines including fraud detection, anomaly detection, oil spillage detection, medical diagnosis, facial recognition. Man...

In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This contest consists of two classification tasks based on data from scientific experiment. The first task is a binary classification task which is to maximize accuracy of classification on an evenly-distributed test data set, given a fully labeled imbalanced training data set. The second task is also ...

Learning from imbalanced data, where the number of observations in one class is significantly rarer than in other classes, has gained considerable attention in the data mining community. Most existing literature focuses on binary imbalanced case while multi-class imbalanced learning is barely mentioned. What’s more, most proposed algorithms treated all imbalanced data consistently and aimed to ...

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuris...

Credit scoring is an important topic, and banks collect different data from their loan applicant to make an appropriate and correct decision. Rule bases are of more attention in credit decision making because of their ability to explicitly distinguish between good and bad applicants. The credit scoring datasets are usually imbalanced. This is mainly because the number of good applicants in a po...

BACKGROUND Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-s...

In classification problems, sampling bias between training data and testing data is critical to the ranking performance of classification scores. Such bias can be both unintentionally introduced by data collection and intentionally introduced by the algorithm, such as under-sampling or weighting techniques applied to imbalanced data. When such sampling bias exists, using the raw classification ...