周志华
主要从事人工智能、机器学习、数据挖掘、模式识别、信息检索、神经计算、进化计算等领域的研究工作。
个性化签名
- 姓名:周志华
- 目前身份:
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学术头衔:
博士生导师
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学科领域:
计算机应用
- 研究兴趣:主要从事人工智能、机器学习、数据挖掘、模式识别、信息检索、神经计算、进化计算等领域的研究工作。
周志华,男,1973年11月生。分别于1996年6月、1998年6月和2000年12月于 南京大学计算机科学与技术系 获学士、硕士和博士学位。2001年1月起留校任教。2002年3月破格晋升副教授,2003年11月被聘任为教授,2004年4月获博士生导师资格。现任 人工智能教研室 主任、机器学习与数据挖掘 (LAMDA) 研究组 负责人。南京航天航空大学 兼职教授、澳大利亚 Deakin大学 名誉研究员、复旦大学智能信息处理重点实验室 学术委员会委员。
目前主要从事人工智能、机器学习、数据挖掘、模式识别、信息检索、神经计算、进化计算等领域的研究工作。曾主持或参加过多项国家、省自然科学基金课题的研究工作。发表国际论文 40余篇。现任 Knowledge and Information Systems 副编辑、Artificial Intelligence in Medicine 、International Journal of Data Warehousing and Mining 、Journal of Computer Science & Technology、 软件学报 等刊编委,ACM/Springer Multimedia Systems 等刊客座编辑,以及包括权威刊物 Artificial Intelligence 和8种IEEE Transactions在内的二十余家国际刊物的审稿专家,国家自然科学基金委员会信息科学部专家评审组成员,香港研究资助局、荷兰科学研究基金会 等机构的课题申请评议专家。曾担任 第7届中国机器学习会议 组织委员会主席、第9届中国机器学习会议 程序委员会共同主席、十余个国际会议程序委员会委员。现任 中国计算机学会 高级会员、人工智能与模式识别专业委员会 副主任,中国人工智能学会理事、机器学习专业委员会副主任兼秘书长、Rough集与软计算专业委员会副主任,江苏省青年科协副会长兼秘书长,IEEE、IEEE计算机协会 会员。
曾获 微软中国研究院 首届“微软学者”奖 (1999)、首届江苏省优秀硕士学位论文奖(2001)、 第七届中创软件人才奖 (2002)、江苏省“青蓝工程”优秀青年骨干教师计划 (2002)、第五届全国优秀博士学位论文奖 (2003)、教育部优秀青年教师资助计划 (2003)、第九届霍英东青年教师基金 (2004)、第六届江苏省优秀科技工作者 (2004)、江苏省十大杰出青年 (2004)等。2003年获 国家杰出青年科学基金。 具体情况见 http://cs.nju.edu.cn/people/zhouzh
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主页访问
2592
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关注数
0
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成果阅读
1557
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成果数
15
【期刊论文】Supervised Nonlinear Dimensionality Reduction for Visualization and Classification
周志华, Xin Geng, De-Chuan Zhan, and Zhi-Hua Zhou, Member, IEEE
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 35, NO.6, DECEMBER 2005,-0001,():
-1年11月30日
When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques. However, when Isomap is applied to real-world data, it shows some limitations, such as being sensitive to noise. In this paper, an improved version of Isomap, namely S-Isomap, is proposed. S-Isomap utilizes class information to guide the procedure of nonlinear dimensionality reduction. Such a kind of procedure is called supervised nonlinear dimensionality reduction. In S-Isomap, the neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. The issimilarity has several good properties which help to discover the true neighborhood of the data and, thus, makes S-Isomap a robust technique for both visualization and classification, especially for real-world problems. In the visualization experiments, S-Isomap is compared with Isomap, LLE, and WeightedIso. The results show that S-Isomap performs the best. In the classification experiments, S-Isomap is used as a preprocess of classification and compared with Isomap, WeightedIso, as well as some other well-established classification methods, including the K-nearest neighbor classifier, BP neural network, J4.8 decision tree, and SVM. The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.
Classification, dimensionality reduction, manifold learning, supervised learning, visualization.,
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【期刊论文】Extracting symbolic rules from trained neural network ensembles
周志华, Zhi-Hua Zhou*, Yuan Jiang and Shi-Fu Chen
AI Communications 16 (2003) 3-15,-0001,():
-1年11月30日
Neural network ensemble can significantly improve the generalization ability of neural network based systems. However, its comprehensibility is even worse than that of a single neural network because it comprises a collection of individual neural networks. In this paper, an approach named REFNE is proposed to improve the comprehensibility of trained neural network ensembles that perform classification tasks. REFNE utilizes the trained ensembles to generate instances and then extracts symbolic rules from those instances. It gracefully breaks the ties made by individual neural networks in prediction. It also employs specific discretization scheme, rule form, and fidelity evaluation mechanism. Experiments show that with different configurations, REFNE can extract rules with good fidelity that well explain the function of trained neural network ensembles, or rules with strong generalization ability that are even better than the trained neural network ensembles in prediction.
Neural networks, neural network ensembles, rule extraction, machine learning, comprehensibility
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134浏览
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【期刊论文】Ensembling neural networks: Many could be better than all☆
周志华, Zhi-Hua Zhou*, Jianxin Wu, Wei Tang
Artificial Intelligence 137 (2002) 239-263,-0001,():
-1年11月30日
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its component neural networks is analyzed from the context of both regression and classification, which reveals that it may be better to ensemble many instead of all of the neural networks at hand. This result is interesting because at present, most approaches ensemble all the available neural networks for prediction. Then, in order to show that the appropriate neural networks for composing an ensemble can be effectively selected from a set of available neural networks, an approach named GASEN is presented. GASEN trains a number of neural networks at first. Then it assigns random weights to those networks and employs genetic algorithm to evolve the weights so that they can characterize to some extent the fitness of the neural networks in constituting an ensemble. Finally it selects some neural networks based on the evolved weights to make up the ensemble. A large empirical study shows that, compared with some popular ensemble approaches such as Bagging and Boosting, GASEN can generate neural network ensembles with far smaller sizes but stronger generalization ability. Furthermore, in order to understand the working mechanism of GASEN, the bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.
Neural networks, Neural network ensemble, Machine learning, Selective ensemble, Boosting, Bagging, Genetic algorithm, Bias-variance decomposition
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203浏览
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【期刊论文】Three perspectives of data mining
周志华, Zhi-Hua Zhou
Artificial Intelligence 143 (2003) 139-146,-0001,():
-1年11月30日
This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on alidity because this perspective cares much for mathematical soundness behind mining methods.
Data mining, Databases, Machine learning, Statistics
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121浏览
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246下载
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【期刊论文】Lung cancer cell identification based on artificial neural network ensembles
周志华, Zhi-Hua Zhou*, Yuan Jiang, Yu-Bin Yang, Shi-Fu Chen
Artificial Intelligence in Medicine 24 (2002) 25-36,-0001,():
-1年11月30日
An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subj ects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e.f. 1l voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell respectily adenocarcinaoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e.plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.
Artificial neural networks, Pattern recognition, Image processing, Computer-aided medical diagnosis, Expert system
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110浏览
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【期刊论文】Multi-Instance Learning Based Web Mining
周志华, ZHI-HUA ZHOU*, KAI JIANG AND MING LI
Applied Intelligence 22, 135-147, 2005,-0001,():
-1年11月30日
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-kNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bag in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-kNN is 81.0% with 71.7% recall and 70.9% precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3% accuracy with 63.4% recall and 66.1% precision.
machine learning, data mining, multi-instance learning, web mining, web index recommendation, text categorization
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【期刊论文】Ensembles of Multi-instance Learners
周志华, Zhi-Hua Zhou and Min-Ling Zhang
ECML 2003, LNAI 2837, pp. 492-502, 2003.,-0001,():
-1年11月30日
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing two famous multi-instance learning algorithms, this paper shows that many supervised learning algorithms can be adapted to multi-instance learning, as long as their focuses are shifted from the discrimination on the instances to the discrimination on the bags. Moreover, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build ensembles of multi-instance learners to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners, and the result achieved by EM-DD ensemble exceeds the best result on the benchmark test reported in literature.
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94浏览
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【期刊论文】Exploiting Unlabeled Data in Content-Based Image Retrieval
周志华, Zhi-Hua Zhou, Ke-Jia Chen, and Yuan Jiang
ECML 2004, LNAI 3201, pp. 525-536, 2004.,-0001,():
-1年11月30日
In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (Cbir), is proposed. This approach combines the merits of semi-supervised learning and active learning. In detail, in each round of relevance feedback, two simple learners are trained from the labeled data, i.e. images from user query and user feedback. Each learner then classifies the unlabeled images in the database and passes the most relevant/irrelevant images to the other learner. After re-training with the additional labeled data, the learners classify the images in the database again and then their classifications are merged. Images judged to be relevant with high confidence are returned as the retrieval result, while these judged with low confidence are put into the pool which is used in the next round of relevance feedback. Experiments show that semi-supervised learning and active learning mechanisms are both beneficial to Cbir.
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周志华, Zhi-Hua Zhou*, Zhao-Qian Chen
Know ledge-Based Systems 15 (2002)) 515-528,-0001,():
-1年11月30日
In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning using symbolic leaming to do qualitative analysis and using neurallearning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary inormation gain r atio critetion in an instance space definde by only original unordered attributes. If unordered attributes cannot further distingguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a speific feedforward neural netword namde FANNC that is trainde in an instance space definde by only original ordered attributes is exploited to accomplish the leaming task. Moreover, this paper distinguishes three kinds of inremental learning tasks. Two incremental leaming procedures designde for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are freaquently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.
Machine learning, Knowledge acquisition, Decision tree, Neural networks, Hybrid learning, Incremental learning, Constructive induction
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【期刊论文】Effcient face candidates selector for face detection
周志华, Jianxin Wu, Zhi-Hua Zhou*
Pattern Recognition 36 (2003) 1175-1186,-0001,():
-1年11月30日
In this paper an e cient face candidates selector is proposed for face detection tasks in still gray level images. The proposed method acts as a selective attentional mechanism. Eye-analogue segments at a given scale are discovered by 5nding regions which are roughly as large as real eyes and are darker than their neighborhoods. Then a pair of eye-analogue segments are hypothesized to be eyes in a face and combined into a face candidate if their placement is consistent with the anthropological characteristic of human eyes. The proposed method is robust in that it can deal with illumination changes and moderate rotations. A subset of the FERET data set and the BioID face database are used to evaluate the proposed method. The proposed face candidates selector is successful in 98.75% and 98.6% cases, respectively.
Face candidates selector, Face detection, Focus of attention, Eye-analogue segment
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