A Novel Data Stream Learning Approach to Tackle One-Sided Label Noise From Verification Latency

Liyan Song1; Shuxian Li1; Leandro L. Minku2; Xin Yao1

10.1109/IJCNN55064.2022.9891911

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

2161-4393

978-1-6654-9526-4

2022 International Joint Conference on Neural Networks (IJCNN)

1-8

18-23 July 2022

Padua, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Many real-world data stream applications suffer from verification latency, where the labels of the training examples arrive with a delay. In binary classification problems, the labeling process frequently involves waiting for a pre-determined period of time to observe an event that assigns the example to a given class. Once this time passes, if such labeling event does not occur, the example is labeled as belonging to the other class. For example, in software defect prediction, one may wait to see if a defect is associated to a software change implemented by a developer, producing a defect-inducing training example. If no defect is found during the waiting time, the training example is labeled as clean. Such verification latency inherently causes label noise associated to insufficient waiting time. For example, a defect may be observed only after the pre-defined waiting time has passed, resulting in a noisy example of the clean class. Due to the nature of the waiting time, such noise is frequently one-sided, meaning that it only occurs to examples of one of the classes. However, no existing work tackles label noise associated to verification latency. This paper proposes a novel data stream learning approach that estimates the confidence in the labels assigned to the training examples and uses this to improve predictive performance in problems with one-sided label noise. Our experiments with 14 real-world datasets from the domain of software defect prediction demonstrate the effectiveness of the proposed approach compared to existing ones.

Data stream learning one-sided label noise verification latency confidence level concept drift clustering just-in-time software defect prediction

First

English

CPCI-S

National Natural Science Foundation of China[62002148] ; EPSRC[EP/R006660/2] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Shenzhen Science and Technology Program[KQTD2016112514355531]

Computer Science ; Engineering ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Neurosciences

WOS:000867070900043

IEEE

1.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, China
2.School of Computer Science, University of Birmingham, UK

Liyan Song,Shuxian Li,Leandro L. Minku,et al. A Novel Data Stream Learning Approach to Tackle One-Sided Label Noise From Verification Latency[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-8.