Fault detection prediction of FAB (wafer fabrication) process in semiconductor manufacturing process is possible that improve product quality and reliability in accordance with the classification performance. However, FAB process is sometimes due to a fault occurs. And mostly it occurs “pass”. Hence, data imbalance occurs in the pass/fail class. If the data imbalance occurs, prediction models a...

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified cla...

In this paper, a novel inverse random under sampling (IRUS) method is proposed for class imbalance problem. The main idea is to severely under sample the negative class (majority class), thus creating a large number of distinct negative training sets. For each training set we then find a linear discriminant which separates the positive class from the negative class. By combining the multiple de...

Classification is one of the most fundamental tasks in the machine learning and data-mining communities. One of the most common challenges faced when trying to perform classification is the class imbalance problem. A dataset is considered imbalanced if the class of interest (positive or minority class) is relatively rare as compared to the other classes (negative or majority classes). As a resu...

This report presents the work completed since the thesis proposal and the revised plan for the future PhD study. Two main issues have been discussed so far: diversity analysis of ensemble models in class imbalance learning, exploration of negative correlation learning on imbalanced data. Experimental design and main conclusions are simply described. More details are included in the two papers i...

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...

Most medical datasets are not balanced in their class labels. Indeed in some cases it has been no ticed that the given class labels do not accurately represent characteristics of the data record. Most existing classification methods tend not to perform well on minority class examples when the dataset is extremely imbalanced. This is because they aim to optimize the overall accuracy without cons...