In this paper, a new wrapper method for feature selection, namely IAFN-FS (Incremental ANalysis Of VAriance and Functional Networks for Feature Selection) is presented. The method uses as induction algorithm the AFN (ANOVA and Functional Networks) learning method; follows a backward non-sequential strategy from the complete set of features (thus allowing to discard several variables in one step...

Automatic speaker verification (ASV) systems are among the biometric systems used in security and telephone-based remote control applications. Recent years have witnessed an increasing trend in research on such systems. These systems usually use high dimension feature vectors and therefore involve high complexity. However, there is a general belief that many of the features used in such systems...

With this PCell wrapper, MGEN layout generators appear like usual SKILL based PCells within DF II. A wrapper PCell must be created for each layout generator. The wrapper PCells just differ in the parameters declarations and the corresponding construction of the layout generator command line. The MGEN environment creates the wrapper PCells automatically. mgenlayoutcds mostran model=nreg w=1u l=1...

Feature selection is often an essential data processing step prior to applying a learning algorithm. The removal of irrelevant and redundant information often improves the performance of machine learning algorithms. There are two common approaches: a wrapper uses the intended learning algorithm itself to evaluate the usefulness of features, while a filter evaluates features according to heurist...

Previous researches on automatic information extraction experienced difficulties in acquiring and representing useful domain knowledge and in coping with the structural heterogeneity among different information sources. As a result, many real-world information sources with complex document structures could not be correctly analyzed. In order to resolve these problems, this paper presents a meth...

Feature selection and classification plays an important role in the design and development of a computer aided detection and diagnostics (CAD) tool for breast cancer detection from mammograms. In literature, the various feature selection methods exists such as filter based, wrapper based, and hybrid methods whose aim is to select the most relevant and minimum redundant features from the extract...

Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from...

There exist two types of wrappers: the string based wrapper such as the LR wrapper, and the tree based wrapper. A tree based wrapper designates extraction regions by nodes on the trees of semistructured documents. The tree based wrapper seems to be more powerful than the string based one. There exist, however, many HTML documents on the Web such that a standard tree based wrapper fails to extra...