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Multiple-Instance Learning with Instance Selection via constructive covering algorithm

Tsinghua Science and Technology 2014年第3期19卷 285-292页

作者： Yanping Zhang Heng Zhang Huazhen Wei Jie Tang Shu Zhao the Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University the Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University1Hefei 230601 China the Department of Computer Science and Technology Tsinghua University

Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training *** bag is made up of several unlabeled instances.A bag is labeled positive if at least one of its instances is positive,otherwise *** multiple-instance learning methods with instance selection ignore the representative degree of the selected *** example,if an instance has many similar instances with the same label around it,the instance should be more representative than *** on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is *** MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a constructive covering algorithm （CCA） to restructure the structure of the original instances of negative *** an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training ***,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted *** results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms.

关键词： multiple-instance learning instance selection constructive covering algorithm maximal Hausdorff

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Multi-Class Decision-Making Method for Decision-Theoretic Rough Sets Based on the constructive covering algorithm

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IEEE ACCESS 2020年 8卷 57833-57848页

作者： Ye, Dajun Liang, Decui Li, Tao Liang, Shujing Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China Sichuan Coll Architectural Technol Deyang 618000 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China

The constructive covering algorithm (CCA) has unique advantages and functions in dealing with multiple-classification problems. To better address the defects and deficiencies of the existing multi-class decision-theoretic rough sets (DTRSs) model, this paper introduces the CCA into DTRSs and constructs a multi-class DTRSs model based on the CCA. First, through the process of machine learning, this method achieves automatic multi-class clustering and effectively addresses the shortcomings of DTRSs in solving multi-class problems, namely, the large amount of computation and the impact of subjective evaluation factors on the inference of threshold values and decision rules as a result of excessive parameters. Second, by optimizing the covering centre and selecting the covering radius, the proposed method improves the efficiency of machine learning. Third, by setting up a distance parameter between covering classes, the method effectively addresses decision redundancy and conflicts when a DTRSs model is used to solve multi-class problems. Then, by organically integrating the cost sensitivity, inter-class distance parameter and class quality function and systematically re-classifying the objects in the boundary domain, it effectively solves the problem of an excessively large boundary domain. Finally, we summarize the decision-making algorithm of the multi-class DTRSs model based on the CCA and demonstrate the feasibility and effectiveness of the method through experiments.

关键词： constructive covering algorithm decision-theoretic rough sets three-way decisions multiclass

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A Three-Way Decisions Model Based on constructive covering algorithm 1

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8th International Conference on Rough Sets and Knowledge Technology (RSKT)

作者： Zhang, Yanping Xing, Hang Zou, Huijin Zhao, Shu Wang, Xiangyang Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Anhui Peoples R China Anhui Elect Engn Profess Tech Coll Hefei 230051 Anhui Peoples R China

ISBN: (数字)9783642412998

ISBN: (纸本)9783642412998;9783642412981

The three-way decisions model divides the universe into three regions, i.e., positive region (POS), boundary region (BND) and negative region (NEG) according to two thresholds. A challenge of the three-way decisions model is how to compute the thresholds that generally rely on the experience of experts. In this paper, we propose a novel three-way decisions model based on constructive covering algorithm(CCA). The new model produces three regions automatically according to the samples and does not need any given parameters. We give a method for constructing coverings from which the three regions are formed. We can classify samples based on the three regions. The experimental results show that the proposed model has great advantage on the classification efficiency and provides a new method to form three regions automatically for the theory of three-way decisions.

关键词： constructive covering algorithm three-way decisions DTRSM three regions parameters

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Voting Based constructive covering algorithm 14

Voting Based Constructive Covering Algorithm

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14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Ding, Zihan Rao, Ruting Yan, Yuanting Zhang, Yanping Anhui University School of Computer Science and Technology Hefei China Anhui University School of Electrical Engirneering and Automation Hefei China

ISBN: (纸本)9781728123486

As one of the typical neural networks from the geometric perspective, constructive covering algorithm (CCA) has received extensive attention since it came out. It constructs the network through randomly initializing the cover center, which also creates the randomization of the algorithm. This paper proposes a method called voting based constructive covering algorithm (V-CCA). The main mechanism of V-CCA is introducing the ensemble technique to overcome the above-mentioned shortcoming. V-CCA trains a group of classifiers each with random initialization instead of a single classifier to generate a stronger learner. Numerical experiments on 16 UCI datasets demonstrate the effectiveness of V-CCA. © 2019 IEEE.

关键词： classification constructive covering algorithm majority voting

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A Parameter-Free Cleaning Method for SMOTE in Imbalanced Classification

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IEEE ACCESS 2019年 7卷 23537-23548页

作者： Yan, Yuanting Liu, Ruiqing Ding, Zihan Du, Xiuquan Chen, Jie Zhang, Yanping Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China

Oversampling is an efficient technique in dealing with class-imbalance problem. It addresses the problem by reduplicating or generating the minority class samples to balance the distribution between the samples of the majority and the minority class. Synthetic minority oversampling technique (SMOTE) is one of the typical representatives. During the past decade, researchers have proposed many variants of SMOTE. However, the existing oversampling methods may generate wrong minority class samples in some scenarios. Furthermore, how to effectively mine the inherent complex characteristics of imbalanced data remains a challenge. To this end, this paper proposes a parameter-free data cleaning method to improve SMOTE based on constructive covering algorithm. The dataset generated by SMOTE is first partitioned into a group of covers, then the hard-to-learn samples can be detected based on the characteristics of sample space distribution. Finally, a pair-wise deletion strategy is proposed to remove the hard-to-learn samples. The experimental results on 25 imbalanced datasets show that our proposed method is superior to the comparison methods in terms of various metrics, such as F-measure, G-mean, and Recall. Our method not only can reduce the complexity of the dataset but also can improve the performance of the classification model.

关键词： Imbalanced data SMOTE oversampling constructive covering algorithm data cleaning

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Multi-classification decision-making method for interval-valued intuitionistic fuzzy three-way decisions and its application in the group decision-making

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第3期12卷 661-687页

With the introduction of the interval-valued intuitionistic fuzzy sets, the interval-valued intuitionistic fuzzy numbers are used instead of precise numbers to provide fuzzy characterization of feature attribute values and misclassification loss function values, which is more in line with the realistic fuzzy decision-making environment. Also, the constructive covering algorithm is introduced into the three-way decisions model, which effectively solves the shortcomings of the traditional decision-theoretic rough sets model in dealing with multi-classification problems, such as too many artificial parameters, complicated computation, redundant decisions, decisional conflicts and excessively large boundary domains. At the same time, in order to avoid the one-sidedness of individual decisions, the group decision-making method is introduced into the preliminarily constructed multi-classification model in this paper to build a multi-classification group decision-making model for interval-valued intuitionistic fuzzy three-way decisions based on the constructive covering algorithm. This model determines the initial weights of feature attributes by the precise weighting method, and determines the expert weights by the grey relational precise weighting method, which effectively achieves the consistency of group decision-making. The decision-making process and rules are also deduced, which expand the model of three-way decisions as well as its practical application value and scope.

关键词： Three-way decisions Multi-classification constructive covering algorithm Interval-valued intuitionistic fuzzy sets Group decision-making

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A novel classification method for paper-reviewer recommendation

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SCIENTOMETRICS 2018年第3期115卷 1293-1313页

作者： Zhao, Shu Zhang, Dong Duan, Zhen Chen, Jie Zhang, Yan-ping Tang, Jie Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Reviewer recommendation problem in the research field usually refers to invite experts to comment on the quality of papers, proposals, etc. How to effectively and accurately recommend reviewers for the submitted papers and proposals is a meaningful and still tough task. At present, many unsupervised recommendation methods have been researched to solve this task. In this paper, a novel classification method named Word Mover's Distance-constructive covering algorithm (WMD-CCA, for short) is proposed to solve the reviewer recommendation problem as a classification issue. A submission or a reviewer is described by some tags, such as keywords, research interests, and so on. First, the submission or the reviewer is represented as some vectors by a word embedding method. That is to say, each tag describing a submission or a reviewer is represented as a vector. Second, the Word Mover's Distance (WMD, for short) method is used to measure the minimum distances between submissions and reviewers. Actually, the papers usually have research field information, and utilizing them well might improve the reviewer recommendation accuracy. So finally, the reviewer recommendation task is transformed into a classification problem which is solved by a supervised learning method- constructive covering algorithm (CCA, for short). Comparative experiments are conducted with 4 public datasets and a synthetic dataset from Baidu Scholar, which show that the proposed method WMD-CCA effectively solves the reviewer recommendation task as a classification issue and improves the recommendation accuracy.

关键词： Reviewer recommendation Classification Word embedding Word Mover's Distance constructive covering algorithm

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A constructive Method for Data Reduction and Imbalanced Sampling 23rd

A Constructive Method for Data Reduction and Imbalanced Samp...

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23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Liu, Fei Yan, Yuanting Anhui Univ Artificial Intelligence Inst Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9789819707973;9789819707980

A large number of training data lead to high computational cost in instanced-based classification. Currently, one of the mainstream methods to reduce data size is to select a representative subset of samples based on spatial partitioning. However, how to select a representative subset while maintaining the overall potential distribution structure of the dataset remains a challenge. Therefore, this paper proposes a constructive data reduction method called constructive covering Sampling (CCS) for classification problems. The CCS does not rely on any relevant parameters. It iteratively partitions the original data space into a group of data subspaces, which contains several samples of the same class, and then it selects representative samples from the data subspaces. This not only maintains the original data distribution structure and reduces data size but also reduces problem complexity and improves the learning efficiency of the classifier. Furthermore, CCS can also be extended as an effective undersampling method (CCUS) to address class imbalance issues. Experiments on 18 KEEL and UCI datasets demonstrate that the proposed method outperforms other sampling methods in terms of F-measure, G-mean, AUC and Accuracy.

关键词： constructive covering algorithm data reduction undersampling class imbalance

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Research on Cost-Sensitive Method for Boundary Region in Three-Way Decision Model

Research on Cost-Sensitive Method for Boundary Region in Thr...

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International Joint Conference on Rough Sets (IJCRS)

作者： Zhang, Yanping Wang, Gang Chen, Jie Fang, Liandi Zhao, Shu Zhang, Ling Wang, Xiangyang Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Anhui Elect Engn Profess Tech Coll Hefei 230051 Anhui Peoples R China

ISBN: (纸本)9783319471600;9783319471594

The three-way decision theory (3WD) is constructed based on the notions of the acceptance, rejection or non-commitment, which can be directly generated by the three regions: positive region (POS), negative region (NEG) and boundary region (BND). At present, how to process the boundary region has become a hot topic in the field of three-way decision theory. Although several methods have been proposed to address this problem, most of them don't take cost-sensitive classification into consideration. In this paper, we adopt a cost-sensitive method to deal with the boundary region. Under the principle of reducing loss of classification, we adjust the border distance which is between sample of boundary region and the cover through introducing a cost-sensitive distance coefficient.. The coefficient. can be automatically calculated according to the distribution characteristics of samples. Compared with other models, experimental results show that our model can obtain high correct classification rate. What's more, our model can reduce loss of classification by improving the recall rate of high cost sample when dealing with the boundary region.

关键词： The three-way decision constructive covering algorithm Processing boundary region Cost-sensitive classification

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Cost-Sensitive Three-Way Decisions Model Based on CCA

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9th International Conference on Rough Sets and Current Trends in Computing (RSCTC)

作者： Zhang, Yanping Zou, Huijin Chen, Xi Wang, Xiangyang Tang, Xuqing Zhao, Shu Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Anhui Elect Engn Profess Tech Coll Hefei 230051 Anhui Peoples R China Jiangnan Univ Wuxi Peoples R China

ISBN: (纸本)9783319086446;9783319086439

The concept of three-way decisions was proposed by Yao and most of three-way decisions models use two thresholds a and beta to partition the universe into three parts: the positive region POS(X), the negative region NEG(X) and the boundary region BND(X). But acquirement of a pair of thresholds is a challenge. In this paper, we propose a new method to get the thresholds of three-way decisions model. We introduce loss function into three-way decisions model based on constructive covering algorithm (CCA). A loss function is interpreted as the costs of making classification decisions. More specifically, for reducing losses of classification, we change the radius of the cover according to the loss function, then we compute the thresholds based on the modification. This paper propose an effective method to compute thresholds alpha and beta, which is according to cost-sensitive three-way decisions model based on CCA.

关键词： constructive covering algorithm three-way decisions loss function DTRSM

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