4.7 Article

Hybrid-ensemble-based interpretable TSK fuzzy classifier for imbalanced data

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research

Miriam Seoane Santos et al.

Summary: The combination of class imbalance and overlap is a challenging issue in machine learning. The lack of a well-formulated definition and measurement of class overlap in real-world domains has hindered the consensus in the research community. This work advocates for a unified view of the problem, discusses key concepts and measurements, and provides an overview of existing data complexity measures and their relationship to class overlap.

INFORMATION FUSION (2023)

Article Computer Science, Artificial Intelligence

Modularizing Deep Learning via Pairwise Learning With Kernels

Shiyu Duan et al.

Summary: This study redefines conventional notions of neural network layers and proposes an alternative modular learning framework for classification, shedding new light on the label requirements of deep learning. Through modular training, high label efficiency is achieved, improving model maintainability and reusability.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Automation & Control Systems

A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data

Guanjin Wang et al.

Summary: In this study, a novel deep-ensemble-level-based TSK fuzzy classifier is proposed for imbalanced data classification tasks. By stacking zero-order TSK fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, promising classification performance and high interpretability are achieved.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Automation & Control Systems

KAT: A Knowledge Adversarial Training Method for Zero-Order Takagi-Sugeno-Kang Fuzzy Classifiers

Bin Qin et al.

Summary: The study aims to enhance the generalization capability of zero-order TSK fuzzy classifiers through a novel knowledge adversarial attack model, which is theoretically justified for its strong generalization capability through dynamic regularization.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Automation & Control Systems

Fuzzy KNN Method With Adaptive Nearest Neighbors

Zekang Bian et al.

Summary: This study proposes a novel classification method based on FKNN called A-FKNN that learns the optimal k value for each testing sample, and a faster version called FA-FKNN is designed. Experimental results show that both A-FKNN and FA-FKNN outperform other methods in terms of classification accuracy, with FA-FKNN having a shorter running time.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Computer Science, Artificial Intelligence

Accelerated Proximal Subsampled Newton Method

Haishan Ye et al.

Summary: The novel algorithm APSSN introduces acceleration techniques to improve computational efficiency of the Newton-type proximal method while maintaining a fast convergence rate. By solving the dual problem using the semismooth Newton method, the scaled proximal mapping is obtained efficiently, contributing to the effectiveness and computational efficiency of the APSSN algorithm. Both theoretical analysis and empirical study support the effectiveness of APSSN for composite function optimization problems.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Computer Science, Software Engineering

Accelerated proximal point method for maximally monotone operators

Donghwan Kim

Summary: This paper introduces an accelerated proximal point method for maximally monotone operators, with computer-assisted proof using the performance estimation problem approach. By incorporating various well-known convex optimization methods, such as the proximal method of multipliers and the alternating direction method of multipliers, the proposed acceleration technique has wide applications. Numerical experiments demonstrate the accelerating behavior of the method.

MATHEMATICAL PROGRAMMING (2021)

Review Computer Science, Information Systems

Deep Learning for Diabetes: A Systematic Review

Taiyu Zhu et al.

Summary: The field of diabetes management has seen significant advancements with the adoption of digital health technologies and the utilization of deep learning approaches. The review of literature highlights the successful applications of deep learning in diabetes diagnosis, glucose management, and the identification of diabetes-related complications.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Computer Science, Artificial Intelligence

Biologically Plausible Fuzzy-Knowledge-Out and Its Induced Wide Learning of Interpretable TSK Fuzzy Classifiers

Bin Qin et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

ADONiS-Adaptive Online Nonsingleton Fuzzy Logic Systems

Direnc Pekaslan et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Fuzzy Ordered c-Means Clustering and Least Angle Regression for Fuzzy Rule-Based Classifier: Study for Imbalanced Data

Jacek M. Leski et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2020)

Article Automation & Control Systems

Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer

Guanjin Wang et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2019)

Article Computer Science, Artificial Intelligence

Imbalanced Deep Learning by Minority Class Incremental Rectification

Qi Dong et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)

Article Computer Science, Artificial Intelligence

Multispectral and hyperspectral image fusion with spatial-spectral sparse representation

Renwei Dian et al.

INFORMATION FUSION (2019)

Article Computer Science, Software Engineering

Sub-sampled Newton methods

Farbod Roosta-Khorasani et al.

MATHEMATICAL PROGRAMMING (2019)

Article Computer Science, Artificial Intelligence

Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

Salman H. Khan et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Deep Takagi-Sugeno-Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules

Yuanpeng Zhang et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Adaptive multi-objective swarm fusion for imbalanced data classification

Jinyan li et al.

INFORMATION FUSION (2018)

Article Computer Science, Artificial Intelligence

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Efficient kNN Classification With Different Numbers of Nearest Neighbors

Shichao Zhang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

Article Automation & Control Systems

Imbalanced TSK Fuzzy Classifier by Cross-Class Bayesian Fuzzy Clustering and Imbalance Learning

Xiaoqing Gu et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2017)

Article Computer Science, Artificial Intelligence

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Enislay Ramentol et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2015)

Article Computer Science, Artificial Intelligence

Least learning machine and its experimental studies on regression capability

Shitong Wang et al.

APPLIED SOFT COMPUTING (2014)

Article Computer Science, Information Systems

Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures

M. J. Gacto et al.

INFORMATION SCIENCES (2011)

Article Computer Science, Artificial Intelligence

Fuzzy logic and related methods as a screening tool for detecting gene regulatory networks

Guy N. Brock et al.

INFORMATION FUSION (2009)