The performance of most practical classifiers improves when correlated or irrelevant features are removed. Machine based classification is thus often preceded by subset selection––a procedure which identifies relevant features of a high dimensional data set. At present, the most widely used subset selection technique is the so-called ‘‘wrapper’’ approach in which a search algorithm is used to i...

The performance of most practical classifiers improves when correlated or irrelevant features are removed. Machine based classification is thus often preceded by subset selection—a procedure which identifies relevant features of a high dimensional data set. At present, the most widely used subset selection technique is the so-called "wrapper" approach in which a search algorithm is used to iden...

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...

Feature selection is an integral step of data mining process to find an optimal subset of features. After examine the problems with both the filter and wrapper approach to feature selection, we propose a two-phase feature selection algorithm of filter and wrapper that can take advantage of both approaches. It begins by running GFSIC(fi1ter approach) to remove irrelevant features, then it runs S...

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact....

The paper investigates techniques for extracting data from HTML sites through the use of automatically generated wrappers. To automate the wrapper generation and the data extraction process, the paper develops a novel technique to compare HTML pages and generate a wrapper based on their similarities and differences. Experimental results on real-life data-intensive Web sites confirm the feasibil...

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wr...

In this paper, we present an approach and a visual tool, called HWrap (Handle Based Wrapper), for creating web wrappers to extract data records from web pages. In our approach, we mainly rely on the visible page content to identify data regions on a web page. In our extraction algorithm, we inspired by the way a human user scans the page content for specific data. In particular, we use text fea...

In the feature subset selection problem a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention while ignoring the rest To achieve the best possible performance with a particular learning algorithm on a particular training set a feature subset selection method should consider how the algorithm and the training set interact We e...