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Feature selection is an important topic in data mining, especially for high dimensional datasets. Filtering techniques in particular have received much attention, but detailed comparisons of their performance is lacking. This work considers three filters using classifier performance metrics and six commonly-used filters. All nine filtering techniques are compared and contrasted using five different...
Feature selection has become the cornerstone of many classification problems. It has been applied in many domains such as Web mining, text categorization, gene expression microarray analysis, image analysis, and combinatorial chemistry. One type of well-studied feature selection methodology is filtering, which is typically divided into ranking and subset evaluation. This work provides an empirical...
There is no general consensus on which classifier performance metrics are better to use as compared to others. While some studies investigate a handful of such metrics in a comparative fashion, an evaluation of specific relationships among a large set of commonly-used performance metrics is much needed in the data mining and machine learning community. This study provides a unique insight into the...
Feature selection is a process of selecting a subset of relevant features for building learning models. It is an important activity for data preprocessing used in software quality modeling and other data mining problems. Feature selection algorithms can be divided into two categories, feature ranking and feature subset selection. Feature ranking orders the features by a criterion and a user selects...
Attribute selection is an important activity in data preprocessing for software quality modeling and other data mining problems. The software quality models have been used to improve the fault detection process. Finding faulty components in a software system during early stages of software development process can lead to a more reliable final product and can reduce development and maintenance costs...
Learning from imbalanced datasets is a well known problem in the data mining community. Many techniques have been proposed to alleviate the problems associated with class imbalance, including data sampling and boosting. While data sampling has received the bulk of the attention from the research community, our results show that boosting often results in better classification performance than even...
The problem of class imbalance in machine learning is quite real and cumbersome when it comes to building a useful and practical classification model. We present a unique insight into addressing class imbalance for classification problems that involve three or more categories, i.e. non-binary. This study is different than related works in the literature because most works focus on addressing class...
It is difficult to learn good classifiers when training data is missing attribute values. Conventional techniques for dealing with such omissions, such as mean imputation, generally do not significantly improve the performance of the resulting classifier. We proposed imputation-helped classifiers, which use accurate imputation techniques, such as Bayesian multiple imputation (BMI), predictive mean...
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