A survey on heterogeneous transfer learning <s> HDP <s> What Is It, and What For?.- Examples.- Integer Programming and LP Relaxation.- Theory of Linear Programming: First Steps.- The Simplex Method.- Duality of Linear Programming.- Not Only the Simplex Method.- More Applications.- Software and Further Reading. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> HDP <s> 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 microarray expression datasets. In addition, given that these datasets exhibit an imbalance between the number of positive and negative examples, the utilization of sampling techniques in the context of feature selection is examined. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> HDP <s> The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd. <s> BIB003 </s> A survey on heterogeneous transfer learning <s> HDP <s> Machine learning classifiers have recently emerged as a way to predict the introduction of bugs in changes made to source code files. The classifier is first trained on software history, and then used to predict if an impending change causes a bug. Drawbacks of existing classifier-based bug prediction techniques are insufficient performance for practical use and slow prediction times due to a large number of machine learned features. This paper investigates multiple feature selection techniques that are generally applicable to classification-based bug prediction methods. The techniques discard less important features until optimal classification performance is reached. The total number of features used for training is substantially reduced, often to less than 10 percent of the original. The performance of Naive Bayes and Support Vector Machine (SVM) classifiers when using this technique is characterized on 11 software projects. Naive Bayes using feature selection provides significant improvement in buggy F-measure (21 percent improvement) over prior change classification bug prediction results (by the second and fourth authors [28]). The SVM's improvement in buggy F-measure is 9 percent. Interestingly, an analysis of performance for varying numbers of features shows that strong performance is achieved at even 1 percent of the original number of features. <s> BIB004 </s> A survey on heterogeneous transfer learning <s> HDP <s> Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance. <s> BIB005 </s> A survey on heterogeneous transfer learning <s> HDP <s> Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. <s> BIB006
Nam and Kim BIB005 proposed an HTL method called Heterogeneous Defect Prediction (HDP) for software defect prediction, though it can also be applied for other tasks. Most software defect prediction studies have been performed to train a model for a project and test it within the same project while using the same set of metrics/attributes. This is known as within-project defect prediction (WPDP). When a new project is developed, it contains little or no historical defect data. Therefore, one can use transfer learning techniques to apply data/models from other source projects and to detect defects in the new target project. This is otherwise known as cross-project defect prediction (CPDP). Most CPDP approaches require both source and target projects to be represented by the same set of metrics as for homogeneous transfer learning though this becomes a limitation as it may be challenging to find source projects which are characterized by the exact same set of metrics as one's new target project. To address such limitation, the proposed method can accept heterogeneous metric sets to transfer knowledge from a source to a target software defect detection task. This approach uses two steps before creating a target classifier: metric selection and metric matching. First, metric selection (a.k.a. feature selection BIB002 denoted in other domains) is applied to the source data to remove redundant and irrelevant features. Techniques applied for this include gain ratio, Chi squared, relief-F, and significance attribute evaluation BIB003 BIB004 . During the experiments, the top 15% of metrics were selected as suggested by BIB003 . Second, metrics are matched between the source and target based on similarities of distribution or correlation. The manner this is done is by measuring the similarity between a source and target metric pair using one of three analyzers (PAnalyzer, KSAnalyzer, or SCoAnalyzer) to calculate a "matching score. " From this, dissimilar pairs are eliminated based on a threshold value and the remaining are organized into groups of unique pairing combinations. Each of these groups has all the combinations of pairings without duplicate metrics. The best combination of metric matching is found by selecting the group with the highest sum of matching scores using weighted bipartite matching BIB001 , thus leaving one remaining group which matches the metrics that are most similar. These scoring techniques include: (1) The PAnalyzer which uses percentile based matching, (2) The KSAnalyzer which uses the p value from the Kolmogorov-Smirnov Test, and (3) The SCoAnalyzer which uses the Spearman's rank correlation coefficient to measure sample correlation. After such procedure, a standard classification algorithm may then be trained on the transformed source to detect defects for target projects. Experiments performed for cross-project defect prediction showed the proposed HDP method effective in most cases, as described by the authors. These results may be misleading though due to certain results not being included in calculations as described by Weiss et al. BIB006 .