ANOVA decomposition is used as the basis for the development of a new wrapper feature subset selection method, in which functional networks are used as the induction algorithm. The performance of the proposed method was tested against several artificial and real data sets. The results obtained are comparable, and even better, in some cases, to those accomplished by other well-known methods, bei...

with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

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Feature selection for video categorization is impractical with existing techniques. In this paper we present a novel algorithm to select a very small subset of image features. We reduce the cardinality of the input data by sorting the individual features by their effectiveness in categorization, and then merging pairwise these features into feature sets of cardinality two. Repeating this sortme...

Feature selection methods are used to find the set of features that yield the best classification accuracy for a given data set. This results in lower training and classification time for a classifier, a support vector machine here, and better classification accuracy. Feature selection, however, may be a time consuming process unfit for real time application. In this paper, we explore a feature...

Web index recommendation systems are designed to help internet users with suggestions for finding relevant information. One way to develop such systems is using the multi-instance learning (MIL) approach: a generalization of the traditional supervised learning where each example is a labeled bag that is composed of unlabeled instances, and the task is to predict the labels of unseen bags. This ...

This paper presents a novel method for extracting information from collections of Web pages across different sites. Our method uses a standard wrapper induction algorithm and exploits named entity information. We introduce the idea of post-processing the extraction results for resolving ambiguous facts and improve the overall extraction performance. Postprocessing involves the exploitation of t...

Wrapper variable selection methods are widely adopted in many applications, among which the design of classifiers. The main problem related to these approaches regards the stability of the selection, namely the exploitation of different training data set can lead to the selection of different variable subsets. This problem is particularly critical in applications where variable selection is use...