Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes...

Mobile phones have become a primary communication device nowadays. In order to maintain proper functionality, various existing security solutions are being integrated into mobile devices. Some of the more sophisticated solutions, such as host-based intrusion detection systems (HIDS) are based on continuously monitoring many parameters in the device such as CPU and memory consumption. Since the ...

Feature Selection techniques usually follow some search stra tegy to select a suitable subset from a set of features Most neural network growing algorithms perform a search with Forward Selection with the ob jective of nding a reasonably good subset of neurons Using this link be tween both elds feature selection and neuron selection we propose and analyze di erent algorithms for the constructio...

Feature selection for various applications has been carried out for many years in many different research areas. However, there is a trade-off between finding feature subsets with minimum length and increasing the classification accuracy. In this paper, a filter-wrapper feature selection approach based on fuzzy-rough gain ratio is proposed to tackle this problem. As a search strategy, a modifie...

Prototype selection, as a preprocessing step in machine learning, is effective in decreasing the computational cost of classification task by reducing the number of retained instances. This goal is obtained by shrinking the level of noise and rejecting the irrelevant data. Prototypes may be also used to understand the data through improving comprehensibility of results. In the paper we discus a...