# Enhanced Label Noise Filtering with Multiple Voting  
[PERSON]  
1 College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; [EMAIL_ADDRESS] (D.G. ); [EMAIL_ADDRESS] (W.Y.) Department of Software, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
[PERSON]  
22344  
4 College of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA 44 College of Technological Innovation, Zayed University, Dubai 144534, UAE; [EMAIL_ADDRESS]  
[PERSON]  
1 College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; [EMAIL_ADDRESS] (D.G. ); [EMAIL_ADDRESS] (W.Y.) Department of Software, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea; [EMAIL_ADDRESS] or [EMAIL_ADDRESS]  
[PERSON]  
4 College of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA 44 College of Technological Innovation, Zayed University, Dubai 144534, UAE; [EMAIL_ADDRESS]  
[PERSON]  
5 Institute of Information Systems, Innopolis University, Tatarstan 420500, Russia; [EMAIL_ADDRESS]  
[PERSON]  
6 Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Gijheung-gu, Yongin-si, Gyeonggi-do 17104, Korea 66 Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Gijheung-gu, Yongin-si, Gyeonggi-dowith a thorough inspection of domain experts. It happens commonly when a consensus is not made during the annotation process by multiple domain experts. Mislabeling is very common in domains requiring rapid development, such as bioinformatics. For example, in a study on breast tumor [9], there existed nine subjective mislabelings among forty-nine features in the training data. Furthermore, mislabeling is also caused by insufficient information available to the expert [10,11]. An example of such information includes the unavailability of data of certain observation results of tests. Physicians are not confident to conclude the crisp diagnosis decision in the presence of partial information. The existence of mislabeled data usually degrades the performance of learning [12, 13, 14, 15, 16, 17]. In general, the goal of a learning algorithm is to search for the best hypothesis from its hypothesis space. In supervised learning, the best hypothesis is usually decided by the correlations between the features and the labels of training data. Therefore, searching for the best hypothesis will be influenced by the mislabeled data, which results in selecting a non-optimal hypothesis. The non-optimal hypothesis can bring a set of negative effects, including classification accuracy reduction, classifier construction time and complexity increase, and others. The approaches dealing with mislabeled data are categorized into two main groups: robust algorithm design [18, 19, 20, 21, 22] and noise filter [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]. The first one is mainly good at developing a novel algorithm that deals with noisy data during model training. The second approach is good at the identification and filtering of mislabeled data before the training. Evidence exists that it is usually difficult to develop a robust algorithm that is insensitive to noisy data. Furthermore, it is revealed that mislabeled noisy data have a severe impact on the approach; even if the design is claimed to be robust. In comparison, filter based approaches have significant performance leverage over the robust algorithm. The core contribution of this work is in the area of filter based approaches. Several filter based approaches are used to deal with mislabeled data, where the ensemble learning based filter (EnFilter) is a widely used approach based on its promising performance [23, 24, 30, 33]. EnFilter leverages others with a unique approach by employing multiple classifiers to identify noises based on their voting.