Junhai Zhai

Hebei University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Sufang Zhang

North China Electric Power University

Xizhao Wang

Shenzhen University

Mengyao Zhai

Nanjing Brain Hospital

Jian-Kai Chen

North China Electric Power University

Zhu Hong

National University of Defense Technology

Junfen Chen

Hebei University

Jiaxing Qi

Harbin University of Science and Technology

Liguang Zang

North China Electric Power University

Zhongyan Li

Yangzhou University

Dingsheng Luo

Peking University

Cooperative Institutions

Hebei University

Chinese Academy of Sciences

Shenzhen University

University of Chinese Academy of Sciences

Tianjin University

North China Electric Power University

Central South University

Tsinghua University

National University of Defense Technology

Chinese Academy of Medical Sciences & Peking Union Medical College

Author Statistics

Papers

Citation

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i-10 index

Research Field

The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers

International Journal of Machine Learning and Cybernetics (2015)

Junhai Zhai Sufang Zhang Chenxi Wang

Sample (material)

10.1007/s13042-015-0478-7

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Citations (101)

FAST FUZZY MULTICATEGORY SVM BASED ON SUPPORT VECTOR DOMAIN DESCRIPTION

International Journal of Pattern Recognition and Artificial Intelligence (2008)

Xizhao Wang Shuxia Lu Junhai Zhai

This paper proposes a fast fuzzy classifier of multicategory support vector machines (FMSVM) based on support vector domain description (SVDD). The main idea is that the proposed FMSVM is obtained by directly considering all data in one optimization formulation, using a fuzzy membership to each input point. The fuzzy membership is determined by support vector domain description (SVDD). For making support vector machine (SVM) more practical, we use an implement of the modified sequential minimal optimization (SMO) that can quickly solve SVM quadratic programming (QP) problems without any extra matrix storage or the use of numerical QP optimization steps at all. Compared with the existing SVMs, the newly proposed FMSVM that uses the L 2 -norm in the objective function shows improvement with regards to accuracy of classification and reduction of the effects of noises and outliers. The experiment also shows the efficiency of the modified SMO for expediting the training of SVM.

Relevance vector machine

Ranking SVM

Margin classifier

10.1142/s0218001408006144

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Citations (51)

Some Insights Into Convolutional Neural Networks

Junhai Zhai Liguang Zang Sufang Zhang

As a kind of deep learning model, convolutional neural networks (CNNs) have greatly boosted the state-of-the-art performance and have found their successful applications in many fields, such as computer version, pattern recognition, natural language processing, etc. Many distinguished CNN models, for example, AlexNet, Google inception net, VGGNet, and so on, have been developed for various tasks. In this paper, we provide some insights into convolutional neural networks in three aspects: activation function, convolution operation, pooling operation, and we also discuss the training of CNNs. The insights presented in this paper will provide the potential readers with further understanding of CNNs.

Pooling

Convolution (computer science)

10.1109/icmlc.2017.8107753

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Citations (6)

MCNet: Multivariate long-term time series forecasting with local and global context modeling

Information Sciences (2024)

Jiaqi Sun Junhai Zhai

Long-term prediction

10.1016/j.ins.2024.120864

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Citations (1)

Information granularity,information entropy and decision tree

Computer Engineering and Applications Journal (2009)

Junhai Zhai Xizhao Wang Sufang Zhang

The information entropy between coarse granularity and fine granularity is comparatively studied,the influence on decision tree caused by coarse granularity and fine granularity is investigated,and the conclusion is provided that the information entropy under coarse granularity is not less than the one under fine granularity.It is shown that the decision tree generated by selecting the expanded attribute under fine granularity is better than the one under coarse granularity.

Granularity

Source

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Citations (1)

Instance selection for big data based on locally sensitive hashing and double-voting mechanism

Advances in Computational Intelligence (2022)

Junhai Zhai Yajie Huang

Bloom filter

10.1007/s43674-022-00033-z

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Citations (4)

Learning with Uncertainty

CRC Press eBooks (2016)

Xizhao Wang Junhai Zhai

Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc. Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.

10.1201/9781315370699

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Citations (12)

Dual-decoder data decoupling training for semi-supervised medical image segmentation

Biomedical Signal Processing and Control (2024)

Bing Wang Taifeng Huang Shuo Yang Ying Yang Junhai Zhai

Decoupling (probability)

Training set

10.1016/j.bspc.2024.106984

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Citations (0)

Discernibility-Matrix Method Based on the Hybrid of Equivalence and Dominance Relations

Lecture notes in computer science (2011)

Li Yan Jin Zhao Na-Xin Sun Xizhao Wang Junhai Zhai

Reduct

Equivalence relation

Dominance (genetics)

Matrix (chemical analysis)

Logical equivalence

10.1007/978-3-642-21881-1_37

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Citations (1)

Research on ordinal regression of interval valued data

Zhu Hong Junhai Zhai Sufang Zhang

Traditional regression algorithms can only deal with non-ordinal regression problems of crisp data. In order to deal with the problem of the regression of interval valued data with the monotonie constraint, an algorithm is proposed in this paper, where the condition attributes are ordinal interval values and the decision attribute is continuous. The proposed algorithm employs the combination of the variance and rank mutual information as heuristic. The advantage of the proposed algorithm is that it can consider both the dispersion degree of the decision attribute value and the monotonie consistency degree that between condition attributes and decision attribute.

Degree (music)

10.1109/icmlc.2015.7340667

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Citations (0)