Feature selection is de ned as a problem to nd a minimum set of M features for an inductive al gorithm to achieve the highest predictive accuracy from the data described by the original N features where M N A probabilistic wrapper model is proposed as another method besides the exhaus tive search and the heuristic approach The aim of this model is to avoid local minima and exhaustive search The...

To simplify the task of constructing wrapper/monitor for the information sources in data warehouse systems, we provide a modularized design method to re-use the code. By substituting some parts of wrapper modules, we can re-use the wrapper on a di€erent information source. For each information source, we also develop a toolkit to generate a corresponding monitor. By the method, we can reduce mu...

We present a service-oriented architecture and a set of techniques for developing wrapper code generators, including the methodology of designing an effective wrapper program construction facility and a concrete implementation, called XWRAPComposer. Our wrapper generation framework has two unique design goals. First, we explicitly separate tasks of building wrappers that are specific to a Web s...

Current mediator and wrapper architectures do not have the exibility to scale to multiple wrapped sources, where some sources may be redundant, and some sources may provide incomplete answers to a query. We propose a meta-wrapper component which is capable of handling multiple wrapped sources, in a particular domain, where the multiple sources provide related information. The meta-wrapper makes...

Due to the size and nature of intrusion detection datasets, systems (IDS) typically take high computational complexity examine features data identify intrusive patterns. Data preprocessing techniques such as feature selection can be used reduce by eliminating irrelevant redundant in dataset. The objective this study is analyze efficiency effectiveness some approaches namely, wrapper-based filte...

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applica...

In this paper, we examine the advantages and disadvantages of filter and wrapper methods for feature selection and propose a new hybrid algorithm that uses boosting and incorporates some of the features of wrapper methods into a fast filter method for feature selection. Empirical results are reported on six real-world datasets from the UCI repository, showing that our hybrid algorithm is compet...

In the wrapper approach for feature selection, a popular criterion used is the leave-one-out estimate of the classification error. While being relatively unbiased, the leave-one-out error estimate is nonetheless known to exhibit a large variance, which can be detrimental especially for small samples. We propose reducing its variance (i.e. smoothing) at two levels. At the first level, we smooth ...