This book discusses the recent advances in natural computation, fuzzy systems and knowledge discovery. Presenting selected, peer-reviewed papers from the 15th International Conference on Natural Computation, Fuzzy Sys...
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ISBN:
(数字)9783030324568
ISBN:
(纸本)9783030324551
This book discusses the recent advances in natural computation, fuzzy systems and knowledge discovery. Presenting selected, peer-reviewed papers from the 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019), held in kunming, China, from 20 to 22 July 2019, it is a useful resource for researchers, including professors and graduate students, as well as R&D staff in industry.
Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction perf...
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Cross-project defect prediction (CPDP) utilizes the existing labeled data in the source project to assist with the prediction of unlabeled projects in the target dataset, which effectively improves the prediction performance and has become a research hotspot in software engineering. At present, CPDP can be categorized into homogeneous cross-project defect prediction and heterogeneous cross-project defect prediction (HDP), in which HDP doesn’t require that the source project and the target project have the same feature space, thus, it is more widely used in the actual CPDP. Most of current HDP methods map the original features to the latent feature space and reduce the inter-project variation by transferring domain-independent features, but the transferring process ignores the use of domain-related features, which affects the prediction performance of the model. Moreover, the mapped latent features are not conducive to the model’s interpretability. Based on these, this paper proposes a heterogeneous defect prediction method based on feature disentanglement (FD-HDP). We disentangle the features using domain-related and domain-independent feature extractors, respectively, to improve the interpretability of the model by maximizing the domain adversarial loss during training and guiding the feature extractors to produce accurate domain-related and domain-independent features. The weighted sum of the prediction results from domain-related and domain-independent predictors is used as the final prediction result of the project during the prediction process, which realizes the combination of domain-independent and domain-related features and effectively improves the prediction performance. In this paper, we conducted experiments using four publicly available defect datasets to construct heterogeneous scenarios. The results demonstrate that the FD-HDP model shows significant advantages over state-of-the-art methods in six metrics.
This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014. The 22 rev...
详细信息
ISBN:
(数字)9783319133652
ISBN:
(纸本)9783319133645
This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014.
The 22 revised full papers were carefully reviewed and selected from 44 submissions. The papers feature a wide range of topics covering both theory, methods and tools as well as their diverse applications in numerous domains.
This book discusses the recent advances in natural computation, fuzzy systems and knowledge discovery. Presenting selected, peer-reviewed papers from the 15th International Conference on Natural Computation, Fuzzy Sys...
详细信息
ISBN:
(数字)9783030325916
ISBN:
(纸本)9783030325909
This book discusses the recent advances in natural computation, fuzzy systems and knowledge discovery. Presenting selected, peer-reviewed papers from the 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019), held in kunming, China, from 20 to 22 July 2019, it is a useful resource for researchers, including professors and graduate students, as well as R&D staff in industry.
Chinese-Vietnamese cross-language event detection aims to cluster texts that describe the same events in Chinese and Vietnamese into corresponding event clusters. However, because Vietnamese is a low-resource language...
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Chinese-Vietnamese cross-language event detection aims to cluster texts that describe the same events in Chinese and Vietnamese into corresponding event clusters. However, because Vietnamese is a low-resource language, directly using multilingual pre-trained models to align event representations in Chinese and Vietnamese texts yields suboptimal results, leading to poor performance in cross-lingual event detection. To address this challenge, we propose a method to enhance cross-lingual event detection between Chinese and Vietnamese by utilizing an aligned knowledge event graph. By leveraging aligned event knowledge, such as personal and place names, to establish correlations between events in different languages, we construct a cross-lingual aligned knowledge event graph. Under the constraint of relational associations, we use contrastive learning to model the similarities and differences between various events, making the representations of the same events in different languages more compact. This approach improves the model’s ability to represent Chinese-Vietnamese cross-lingual event texts and enhances the effectiveness of cross-lingual event detection. Experimental results demonstrate that our method, on multiple multilingual pre-trained models, achieves significant improvements across evaluation metrics such as normalized mutual information, adjusted normalized mutual information, and the adjusted rand coefficient.
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