Feature selection is a pre-processing technique used for eliminating the irrelevant and redundant features which results in enhancing the performance of the classifiers. When a dataset contains more irrelevant and redundant features, it fails to increase the accuracy and also reduces the performance of the classifiers. To avoid them, this paper presents a new hybrid feature selection method usi...

Abstract Real-world problems are commonly characterized by a high feature dimensionality, which hinders the modelling and descriptive analysis of data. However, some these data may be irrelevant or redundant for learning process. Different approaches can used to reduce this information, improving not only speed building models but also their performance interpretability. In review, we focus on ...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...