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A large system often goes through multiple software project development cycles, in part due to changes in operation and development environments. For example, rapid turnover of the development team between releases can influence software quality, making it important to mine software project data over multiple system releases when building defect predictors. Data collection of software attributes are...
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 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...
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...
There are several performance metrics that have been proposed for evaluating a classification model, e.g., accuracy, error rates, precision, recall, etc. While it is known that evaluating a classifier on only one performance metric is not advisable, the use of multiple performance metrics poses unique comparative challenges for the analyst. Since different performance metrics provide different perspectives...
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...
Erroneous attribute values can significantly impact learning from otherwise valuable data. The learning impact can be exacerbated by the class imbalanced training data. We investigate and compare the overall learning impact of sampling such data by using four distinct performance metrics suitable for models built from binary class imbalanced data. Seven relatively free of noise, class imbalanced software...
Two common challenges data mining and machine learning practitioners face in many application domains are unequal classification costs and class imbalance. Most traditional data mining techniques attempt to maximize overall accuracy rather than minimize cost. When data is imbalanced, such techniques result in models that highly favor the over represented class, the class which typically carries a...
Collaborative filtering (CF) is one of the most effective types of recommender systems. As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse rating data. In this paper, we propose two imputed neighborhood based collaborative filtering (INCF) algorithms: imputed nearest neighborhood CF (INN-CF)...
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...
This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was utilized for this study because of its strong performance relative to well-known...
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