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Multi-correntropy f...
Multi-correntropy fusion based fuzzy system for predicting DNA N4-methylcytosine sites
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- Ding, Yijie (författare)
- University of Electronic Science and Technology of China, Quzhou, China
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- Tiwari, Prayag, 1991- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Guo, Fei (författare)
- Central South University, Changsha, China
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- Zou, Quan (författare)
- University of Electronic Science and Technology of China, Chengdu, China; University of Electronic Science and Technology of China, Chengdu, China
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(creator_code:org_t)
- Amsterdam : Elsevier, 2023
- 2023
- Engelska.
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 100, s. 1-10
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The identification of DNA N4-methylcytosine (4mC) sites is an important field of bioinformatics. Statistical learning methods and deep learning have been applied in this direction. The previous methods focused on feature representation and feature selection, and did not take into account the deviation of noise samples for recognition. Moreover, these models were not established from the perspective of prediction error distribution. To solve the problem of complex error distribution, we propose a maximum multi-correntropy criterion based kernelized higher-order fuzzy inference system (MMC-KHFIS), which is constructed with multi-correntropy fusion. There are 6 4mC and 8 UCI data sets are employed to evaluate our model. The MMC-KHFIS achieves better performance in the experiment. © 2023
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- 4mC
- DNA N4-methylcytosine
- Fuzzy model
- Multi-correntropy fusion
- Sequence classification
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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