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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...
In this paper, we study the learning impact of data sampling followed by attribute selection on the classification models built with binary class imbalanced data within the scenario of software quality engineering. We use a wrapper-based attribute ranking technique to select a subset of attributes, and the random undersampling technique (RUS) on the majority class to alleviate the negative effects...
The application of feature ranking to software engineering datasets is rare at best. In this study, we consider wrapper-based feature ranking where nine performance metrics aided by a particular learner are evaluated. We consider five learners and take two different approaches, each in conjunction with one of two different methodologies: 3-fold Cross-Validation (CV) and 3-fold Cross-Validation Risk...
Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set...
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...
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