BibTeX:

@article{IJIRSTV1I10005,
     title={Ensembles Method For One Class Classification Using Convex Hull Polytope Model },
     author={Jay Bhatt and Chaita Jani},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={1},
     number={10},
     pages={19--24},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV1I10005.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Classification is a data mining task that allocated similar data to categories or classes. One of the most general methods for classification is ensemble method which refers supervised learning. After generating classification rules we can apply those rules on unidentified data and achieve the results. In one-class classification it is supposed that only information of one of the classes, the target class, is available. In an ensemble classification system, different base classifiers are combined in order to obtain a classifier with higher performance. In this work, a new one-class classification ensemble strategy called Approximate Polytope Ensemble is presented. The geometrical theory of convex hull is used to define the boundary of the target class defining the problem. 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, convex hull, polytope