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 ...

This work explores the usage of Linked Data for Web scale Information Extraction, with focus on the task of Wrapper Induction. We show how to effectively use Linked Data to automatically generate training material and build a self-trained Wrapper Induction method. Experiments on a publicly available dataset demonstrate that for covered domains, our method can achieve F measure of 0.85, which is...

Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before softwa...

At present, the mediator/wrapper integration methods are widely used in ontology based data integration because they solve the data update problems of data warehouse method. The key of this method is building of mapping from the global ontology in mediator to the local ontology in wrapper. This article analyzes the general mapping methods and designs a SPARQL query driven Global Local as View (...

We investigate an important issue of a meta-algorithm for selecting variables in the framework of microarray data. This wrapper method starts from any classification algorithm and weights each variable (i.e. gene) relative to its efficiency for classification. An optimization procedure is then inferred which exhibits important genes for the studied biological process. Theory and application wit...

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....

Distributed systems require declarative access to diverse information sources. One approach to solving this heterogeneous distributed database problem is based on mediator architectures. In these architectures, mediators accept queries from users, process them with respect to wrappers, and return answers. Wrapper provide access to underlying sources. To eeciently process queries, the mediator m...

We investigate an important issue of a meta-algorithm for selecting variables in the framework of microarray data. This wrapper method starts from any classification algorithm and weights each variable (i.e. gene) relative to its efficiency for classification. An optimization procedure is then inferred which exhibits important genes for the studied biological process. Theory and application wit...

For microarray based cancer classification, feature selection is a common method for improving classifier generalisation. Most wrapper methods use cross validation methods to evaluate feature sets. For small sample problems like microarray, however, cross validation methods may overfit the data. In this paper, we propose a Structural Risk Minimisation (SRM) based method for gene selection in ca...

A wrapper feature selection method based on Immune Clonal Algorithm for SVM is presented and applied to 1-D images recognition of radar targets in this paper. In the proposed method, the cross-validation is used for feature evaluation in wrapper feature selection step for SVMs. And Immune Clonal Algorithm, which is characterized by rapid convergence to global optimal solution, is applied to fin...