BibTeX:

@article{IJIRSTV1I7018,
     title={A SURVEY ONE CLASS CLASSIFICATION USING ENSEMBLES METHOD},
     author={JAY BHATT and Nikita S Patel},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={7},
     pages={19--23},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I7018.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

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 classes, the target class, is available. This means that just example objects of the target class can be used and that no information about the other class of outlier objects is present. In One Class Classification (occ) problem the negative class is either absent or improperly sampled. There are several classification mechanisms that can be used. In an ensemble classification system, different base classifiers are combined in order to obtain a classifier with higher performance. The most widely used ensemble learning algorithms are AdaBoost and Bagging. The process of ensemble learning method can be divided into three phases: the generation phase, in which a set of candidate models is induced, the pruning phase, to select of a subset of those models and the integration phase, in which the output of the models is combined to generate a prediction.

Bagging, Boosting, Classification, Ensembles, One Class Classification, Positive and Unlabeled Data