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 ...

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...

In Data mining Classification is a data mining function that allocated similar data to categories or classes. One of the most common methods for classification is ensemble method which refers supervised learning. After generating classification rules we can apply those rules on unknown data and reach to the results. In one-class classification it is assumed that only information of one of the c...

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/ novelty ...

Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product ...

It has been demonstrated in the literature that the combining of different biometric traits is a powerful tool to overcome the limitations imposed by a single biometric system. The fusion of different systems can be approached in different ways. In this work, we consider the pattern classification approach, where the scores of the various systems are used as features to feed the classifiers. Mo...