The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
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...
A common problem for data mining and machine learning practitioners is class imbalance. When examples of one class greatly outnumber examples of the other class (es), traditional machine learning algorithms can perform poorly. Random undersampling is a technique that has shown great potential for alleviating the problem of class imbalance. However, undersampling leads to information loss which can...
Constructing classification models using skewed training data can be a challenging task. We present RUSBoost, a new algorithm for alleviating the problem of class imbalance. RUSBoost combines data sampling and boosting, providing a simple and efficient method for improving classification performance when training data is imbalanced. In addition to performing favorably when compared to SMOTEBoost (another...
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...
Boosting has been shown to improve the performance of classifiers in many situations, including when data is imbalanced. There are, however, two possible implementations of boosting, and it is unclear which should be used. Boosting by reweighting is typically used, but can only be applied to base learners which are designed to handle example weights. On the other hand, boosting by resampling can be...
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...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.