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We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are statistically significant. In order to make decision trees robust, we begin by expressing Information Gain, the metric used in C4.5, in terms of confidence of a rule. This allows us to immediately explain why Information...

Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In ...

With imbalanced data a classifier built using all of the data has the tendency the ignore the minority class. To overcome this problem, we propose to use an ensemble classifier constructed on the basis of a large number of relatively small and balanced subsets, where representatives from both patterns are to be selected randomly. As an outcome, the system produces the matrix of linear regressio...

We present our experience in applying a rule induction technique to an extremely imbalanced pharmaceutical data set. We focus on using a variety of performance measures to evaluate a number of rule quality measures. We also investigate whether simply changing the distribution skew in the training data can improve predictive performance. Finally, we propose a method for adjusting the learning al...

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 ...

Most of the existing methods for unbalanced data classification only consider about the situation of imbalance between classes but don't consider about the situation within the class, thus affect the final classification results. In order to eliminate the imbalance within the class, put forward the cluster algorithms based on DBSACN algorithm to process the imbalance problem within the class. T...

The class imbalance problem causes a classier to overt the data belonging to the class with the greatest number of training examples. The purpose of this paper is to argue that methods that equalize class membership are not as e ective as possible when applied blindly and that improvements can be obtained by adjusting for the within-class imbalance. A guided resampling technique is proposed and...

Nowadays learning from imbalanced data sets are a relatively a very critical task for many data mining applications such as fraud detection, anomaly detection, medical diagnosis, information retrieval systems. The imbalanced learning problem is nothing but unequal distribution of data between the classes where one class contains more and more samples while another contains very little. Because ...