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13th International Conference on machine learning and Cybernetics, ICMLC 2014

作者： Zhu, Hong Zhai, Junhai Wang, Shanshan Wang, Xizhao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071002 China

ISBN: (纸本)9783662456514

Traditional decision tree algorithms for interval valued data only can deal with non-ordinal classification problems. In this paper, we presented an algorithm to solve the ordinal classification problems, where both the condition attributes with interval values and the decision attributes meet the monotonic requirement. The algorithm uses the rank mutual information to select extended attributes, which guarantees that the outputted decision tree is monotonic. The proposed algorithm is illustrated by a numerical example, and a monotonically consistent decision tree is generated. The design of algorithm can provide some useful guidelines for extending real-vauled to interval-valued attributes in ordinal decision tree induction. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Decision trees

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An image retrieval method about ancient Chinese characters within local area

An image retrieval method about ancient Chinese characters w...

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Tian, Xue-Dong Huang, Juan Yang, Fang Ha, Yan College of Mathematics and Computer Science Hebei University Baoding Hebei China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

ISBN: (纸本)9781457717017

Ancient Chinese characters normally have complex structures which are composed of many strokes. Different characters may show a similar shape which results in unsatisfactory answers for their image retrieving using the traditional methods. In this paper, an image retrieval algorithm for ancient Chinese characters is proposed based on the local area of characters. First of all, according to the need of users, the regions needed to be matched are marked on the sample of ancient Chinese character image. Then we match the image by comparing the Euclidean distance of feature parameters between sample image and database images. Finally, the ancient images which users need are found. The experimental results show that the proposed method could improve the efficiency of correlative research. © 2011 IEEE.

关键词： Image retrieval

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CET4 passing rate analysis based on fuzzy decision tree induction and active learning

CET4 passing rate analysis based on fuzzy decision tree indu...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Qiao, Qing-Shui Wang, Hai-Tao Wang, Zhen-Yu Zhai, Jun-Hai Dept. of English Hebei Institute of Civil Engineering and Architecture Zhangjiakuo City Hebei China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding City China

ISBN: (纸本)9781457703065

College English Test Band Four (CET4) in China has been a significant impact on evaluating the English preliminary level of a college student or a class. How to improve the college English teaching and go further to raise passing rate of CET4 are a challenge for many colleges and universities. This paper makes an attempt to quantitatively analyze the CET4 and exam-related factors by using fussy decision tree technique and active learning based on uncertainty. Several features are selected to formulate this problem. The weighted margin is proposed as the new uncertainty measure criterion for unlabeled instance, and a density measure is introduced for avoiding selecting isolated instances. Experiments and simulations on different classes of students show the proposed quantitative analysis method is feasible and effective, which can provide teachers with some useful guidelines for how to improve the college English teaching. © 2011 IEEE.

关键词： Decision trees

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Integrating local information-based link prediction algorithms with owa operator

Integrating local information-based link prediction algorith...

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3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014

作者： Liu, James N.K. He, Yu-Lin Hu, Yan-Xing Wang, Xi-Zhao Shiu, Simon C.K. Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9789897580185

The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs) which have been proposed to handle this essential and crucial problem in the social network analysis. However, they can not adequately consider the so-called local information: The degrees of x and y, the number of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words, not any LILPA takes into account all the local information simultaneously. This limits the performances of LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different LILPAs with aggregation weights which are determined with maximum entropy method. The final experimental results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual LILPAs. Copyright © 2014 SCITEPRESS.

关键词： Maximum entropy methods

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Audio retrieval based on Chinese keyword search in relational databases

Audio retrieval based on Chinese keyword search in relationa...

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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Zhu, Bo Yan Liu, Guang Zhu, Liang College of Art Hebei University Baoding Hebei 071002 China Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9783037857700

In this paper, we propose a new method based on Chinese keyword search to select the WAV or MP3 files in audio post-production. First, we listen to each file and label it with Chinese characters, and then classify and store the files in a relational database system. Then, we use the techniques of Chinese keyword search to match query characters and the tuple characters quickly, and to compute similarities between the query and candidate tuples. For the characteristics of Chinese keyword search, we present a ranking strategy and an algorithm to refine the candidate tuples resulting from the first round matching, and finally get top-N results of audio files. The experimental results show that our method is efficient and effective. © (2013) Trans Tech Publications, Switzerland.

关键词： Relational database systems

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Maximizing bi-mutual information of features for self-supervised deep clustering

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Advances in computational intelligence 2021年第1期2卷 1-11页

作者： Zhao, Jiacheng Chen, Junfen Meng, Xiangjie Zhai, Junhai Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Baoding China

Self-supervised learning based on mutual information makes good use of classification models and label information produced by clustering tasks to train networks parameters, and then updates the downstream clustering assignment with respect to maximizing mutual information between label information. This kind of methods have attracted more and more attention and obtained better progress, but there is still a larger improvement space compared with the methods of supervised learning, especially on the challenge image datasets. To this end, a self-supervised deep clustering method by maximizing mutual information is proposed (bi-MIM-SSC), where deep convolutional network is employed as a feature encoder. The first term is to maximize mutual information between output-feature pairs for importing more semantic meaning to the output features. The second term is to maximize mutual information between an input image and its feature generated by the encoder for keeping the useful information of an original image in latent space as possible. Furthermore, pre-training is carried out to further enhance the representation ability of the encoder, and the auxiliary over-clustering is added in clustering network. The performance of the proposed method bi-MIM-SSC is compared with other clustering methods on the CIFAR10, CIFAR100 and STL10 datasets. Experimental results demonstrate that the proposed bi-MIM-SSC method has better feature representation ability and provide better clustering results.

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Robust smooth one-class support vector machine

Robust smooth one-class support vector machine

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International Conference on Information Technology and Electronic Commerce

作者： Jin-Kou Hu Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei Province China

ISBN: (纸本)9781479953004

In this paper, a novel one-class classification approach, namely, robust smooth one-class support vector machine (RSOCSVM) is proposed. The proposed method can efficiently enhance the anti-noise ability of the traditional one-class support vector machine (OCSVM). Utilizing the smooth technique, RSOCSVM reformulates the quadratic programming problem of OCSVM as an unstrained optimization format. Moreover, half-quadratic minimization is used to solve the obtained unstrained optimization problem. Experimental results on two synthetic data sets and nine benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM.

关键词： Optimization Support vector machines Robustness Accuracy Minimization Training Benchmark testing

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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Journal of Ambient intelligence and Humanized Computing 2023年第4期14卷 4313-4327页

作者： Pirozmand, Poria Jalalinejad, Hoda Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Li, Yingqiu Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding071002 China Department of Mathematics and Computer Science Bandar Abbas Branch Islamic Azad University Bandar Abbas Iran Department of Computer Science University of Regina Regina Canada Tehran Iran

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cloud computing

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Correntropy based self-organizing map

Correntropy based self-organizing map

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Qing-Zhen Shang Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Province China

ISBN: (纸本)9781509003914

Self-organizing map (SOM) is regarded as a type of feedfoward neural network. It has been successfully used for unsupervised learning. However, the objective function of the traditional SOM relies on the mean squared error (MSE) criterion, which makes the performance of SOM become poor in the presence of noise. In the paper, correntropy based measure is proposed to substitute MSE to enhance the anti-noise ability of SOM. Moreover, the half-quadratic optimization technique is utilized to deal with the corresponding optimization problem. Experimental results demonstrate that the proposed method achieves better anti-noise performance in comparison with the traditional SOM.

关键词： Neurons Distributed databases Optimization Topology Kernel Cybernetics Self-organizing feature maps

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Index construction for mathematical expression retrieval based on Trie tree 4th

Index construction for mathematical expression retrieval bas...

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4th International Conference on Electronics, Communications and Networks, CECNet2014

作者： Sun, Jing Tian, Xuedong Liu, Dazhong Zhang, Zhiming School of Computer Science and Technology Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics and Information Science Hebei University Baoding China

ISBN: (纸本)9781138028302

With two-dimensional structures, mathematical expressions deliver more information than normal text not only with symbols but also their spatial arrangements. Users of mathematical retrieval systems need more search modes, such as fuzzy matching mode, rather than only exact matching mode to fit the features of mathematical expressions. Therefore, more and more researchers focus on how to retrieve mathematical expressions on the levels of structural similarity matching and synonyms matching. In this paper, an index structure based on Trie tree is designed oriented to measure the structural similarity of mathematical expressions. Firstly, the existing math indexing and matching method are introduced. Then, a stack algorithm of extracting information of math operators in LaTeX format is given. Finally, an index method based on Trie Tree is proposed for structural matching of mathematical expressions. The experimental analysis shows the effectiveness of the index method in this paper. © 2015 Taylor & Francis Group, London.

关键词： Search engines

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