A hybrid feature selection method called SU-GA-W is proposed to make full use of advantages of filter and wrapper methods. This method falls into two phases. The filter phase removes features with lower SU and guides the initialization of GA population; the wrapper phase searches the final feature subset. The effectiveness of this algorithm is demonstrated on various data sets.

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...

Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms ar...

We present a language extension, which integrates in a Java like language a mechanism for dynamically extending object behaviors without changing their type. Our approach consists in moving the addition of new features from class (static) level to object (dynamic) level: the basic features of entities (representing their structure) are separated from the additional ones (wrapper classes whose i...

Microarrays allow researchers to monitor the gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotype and conditions. Selecting a small subset of discriminate genes from thousands of genes is important for accurate classification of diseases and phenotypes. Many methods have been proposed to find subsets of genes with maximum relevance and min...

Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to wrapper methods, however wrapper methods are computationally more demanding than filter methods. A novel filter feature selection method based on mutu...

13 Selection of input features plays an important role in developing models for short14 term load forecasting (STLF). Previous studies along this line of research have focused 15 pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid 16 selection scheme that includes both filter and wrapper methods in constructing an 17 appropriate pool of features, coupled with the...

The topic of data warehousing encompasses architec-tures, algorithms, and tools for bringing together selected data from multiple databases or other information sources into a single repository, called a data warehouse , suitable for direct querying or analysis. In recent years data warehousing has become a prominent buz-zword in the database industry, but attention from the database research c...

Many machine learning applications require classi ers that minimize an asymmetric loss function rather than the raw misclassi cation rate. We study methods for modifying C4.5 to incorporate arbitrary loss matrices. One way to incorporate loss information into C4.5 is to manipulate the weights assigned to the examples from di erent classes. For 2-class problems, this works for any loss matrix, b...