The prediction of defects in a target project based on data from external projects is called Cross-Project Defect Prediction (CPDP). Several methods have been proposed to improve the predictive performance of CPDP models. However, there is a lack of comparison among state-of-the-art methods. Moreover, previous work has shown that the most suitable method for a project can vary according to the project being predicted. This makes the choice of which method to use difficult. We provide an extensive experimental comparison of 31 CPDP methods derived from state-of-the-art approaches, applied to 47 versions of 15 open source software projects. Four methods stood out as presenting the best performances across datasets. However, the most suitable among these methods still varies according to the project being predicted. Therefore, we propose and evaluate a meta-learning solution designed to automatically select and recommend the most suitable CPDP method for a project. Our results show that the meta-learning solution is able to learn from previous experiences and recommend suitable methods dynamically. When compared to the base methods, however, the proposed solution presented minor difference of performance. These results provide valuable knowledge about the possibilities and limitations of a meta-learning solution applied for CPDP.
In this study we highlight a problem transformation method called Binary Relevance (BR) @cite_66 . In this method, the multi-label problem is transformed into @math binary classifiers, one for each different label in @math . Then, a dataset @math , @math , is created for each label @math containing all examples of the original dataset. Each example @math in @math is labelled positively if @math and negatively otherwise.
@cite_66
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In multi-label classification, each example can be associated with multiple labels simultaneously. The task of learning from multilabel data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multilabel learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. In this paper, we consider a simple approach which can be used to explore labels dependency aiming to accurately predict label combinations. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance. Multi-label classification refers to learning tasks with each instance belonging to one or more classes simultaneously. It arose from real-world applications such as information retrieval, text categorization and functional genomics. Currently, most of the multi-label learning methods use the strategy called binary relevance, which constructs a classifier for each unique label by grouping data into positives (examples with this label) and negatives (examples without this label). With binary relevance, an example with multiple labels is considered as a positive data for each label it belongs to. For some classes, this data point may behave like an outlier confusing classifiers, especially in the cases of well-separated classes. In this paper, we first introduce a new strategy called soft relevance, where each multi-label example is assigned a relevance score to the labels it belongs to. This soft relevance is then employed in a voting function used in a k nearest neighbor classifier. Furthermore, a voting-margin ratio is introduced to the k nearest neighbor classifier for better performance. We compare the proposed method to other multi-label learning methods over three multi-label datasets and demonstrate that the proposed method provides an effective way to multi-label learning. The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity. The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.