This paper presents a wrapper method for feature selection that combines Lazy Learning, racing and subsampling techniques. Lazy Learning (LL) is a local learning technique that, once a query is received, extracts a prediction by locally interpolating the neighboring examples of the query which are considered relevant according to a distance measure. Local learning techniques are often criticize...

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...

Many machine learning applications require classifiers that minimize an asymmetric loss function rather than the raw misclassification rate. We introduce a wrapper method for data stratification to incorporate arbitrary cost matrices into learning algorithms. One way to implement stratification for C4.5 decision tree learners is to manipulate the weights assigned to the examples from different ...

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...

We present general-purpose methods for recognizing certain types of structure in HTML documents. The methods are implemented using WHIRL, a "soft" logic that incorporates a notion of textual similarity developed in the information retrieval community. In an experimental evaluation on 82 Web pages, the structure ranked first by our method is "meaningful"--i.e., a structure that was used in a han...

Data mining is a form of knowledge discovery required for solving problems in a specific domain. Classification is a technique used for discovering class labels of unknown data. Different methods for classification exists like bayesian, decision trees, rule based, neural networks etc. Before applying any mining technique, irrelevant and redundant features needs to be removed. Filtering is done ...