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Shengli Wu

Bio: Shengli Wu is an academic researcher from Jiangsu University. The author has contributed to research in topics: Sensor fusion & Computer science. The author has an hindex of 18, co-authored 89 publications receiving 1158 citations. Previous affiliations of Shengli Wu include University of Strathclyde & Ulster University.


Papers
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Journal ArticleDOI
TL;DR: This paper presents an analysis of the behaviour of several well-known methods such as CombSum and CombMNZ for fusion of multiple information retrieval results and predicts the performance of the data fusion methods.
Abstract: The data fusion technique has been investigated by many researchers and has been used in implementing several information retrieval systems. However, the results from data fusion vary in different situations. To find out under which condition data fusion may lead to performance improvement is an important issue. In this paper, we present an analysis of the behaviour of several well-known methods such as CombSum and CombMNZ for fusion of multiple information retrieval results. Based on this analysis, we predict the performance of the data fusion methods. Experiments are conducted with three groups of results submitted to TREC 6, TREC 2001, and TREC 2004. The experiments show that the prediction of the performance of data fusion is quite accurate, and it can be used in situations very different from the training examples. Compared with previous work, our result is more accurate and in a better position for applications since various number of component systems can be supported while only two was used previously.

91 citations

Proceedings ArticleDOI
09 Mar 2003
TL;DR: The experimental results showed that the proposed methods are effective, and in many cases are more effective than Soboroff at al.'s method.
Abstract: In this paper we present some new methods of ranking information retrieval systems without relevance judgement. The common ground of these methods is using a measure we called reference count. An extensive experimentation was conducted to evaluate the effectiveness of the proposed methods using various different standards Information Retrieval evaluation measures for the ranking, like average precision, R-precision, and precision and different document levels. We also compared the effectiveness of the proposed methods with the method proposed by Soboroff et al. The experimental results showed that the proposed methods are effective, and in many cases are more effective than Soboroff at al.'s method.

91 citations

Journal ArticleDOI
TL;DR: Experimental results show that the proposed approach could construct a neural network ensemble with better generalization performance than that of each individual in the ensemble combining with all the other individuals, and than those of the ensembles with simply averaged weights.
Abstract: This paper, with an aim at improving neural networks' generalization performance, proposes an effective neural network ensemble approach with two novel ideas. One is to apply neural networks' output sensitivity as a measure to evaluate neural networks' output diversity at the inputs near training samples so as to be able to select diverse individuals from a pool of well-trained neural networks; the other is to employ a learning mechanism to assign complementary weights for the combination of the selected individuals. Experimental results show that the proposed approach could construct a neural network ensemble with better generalization performance than that of each individual in the ensemble combining with all the other individuals, and than that of the ensembles with simply averaged weights.

86 citations

Journal ArticleDOI
01 Jan 2002
TL;DR: This paper discusses key access control requirements for application data in workflow applications using examples from the healthcare domain, introduces a classification of application data used in workflow systems by analyzing their sources, and proposes a comprehensive data authorization and access control mechanism for WfMSs.
Abstract: Workflow Management Systems (WfMSs) are used to support the modeling and coordinated execution of business processes within an organization or across organizational boundaries. Although some research efforts have addressed requirements for authorization and access control for workflow systems, little attention has been paid to the requirements as they apply to application data accessed or managed by WfMSs. In this paper, we discuss key access control requirements for application data in workflow applications using examples from the healthcare domain, introduce a classification of application data used in workflow systems by analyzing their sources, and then propose a comprehensive data authorization and access control mechanism for WfMSs. This involves four aspects: role, task, process instance-based user group, and data content. For implementation, a predicate-based access control method is used. We believe that the proposed model is applicable to workflow applications and WfMSs with diverse access control requirements.

78 citations

Journal ArticleDOI
TL;DR: An updated view on the different modifications of these techniques, which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting are presented.
Abstract: The combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts which have been made to improve their performance. Within this paper we present and compare an updated view on the different modifications of these techniques which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting. In addition we provide a review of different ensemble selection methods based on both static and dynamic approach. We present some new directions which have been adopted in the area of classifier ensembles from a range of recently published studies. In order to provide better understanding on how the ensembles work some existing theoretical studies have been reviewed in the paper.

68 citations


Cited by
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Journal ArticleDOI
TL;DR: A multiobjective deep belief networks ensemble (MODBNE) method that employs a multiobjectives evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives is proposed.
Abstract: In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most effective and reliable maintenance policy. Prognostics, as one of the key enablers of CBM, involves the core task of estimating the remaining useful life (RUL) of the system. Neural networks-based approaches have produced promising results on RUL estimation, although their performances are influenced by handcrafted features and manually specified parameters. In this paper, we propose a multiobjective deep belief networks ensemble (MODBNE) method. MODBNE employs a multiobjective evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives. The eventually evolved DBNs are combined to establish an ensemble model used for RUL estimation, where combination weights are optimized via a single-objective differential evolution algorithm using a task-oriented objective function. We evaluate the proposed method on several prognostic benchmarking data sets and also compare it with some existing approaches. Experimental results demonstrate the superiority of our proposed method.

569 citations

Journal ArticleDOI
TL;DR: A new measure of the similarity between incomplete rankings, namely rank-biased overlap (RBO), is proposed, based on a simple probabilistic user model and extended to handle tied ranks and rankings of different lengths.
Abstract: Ranked lists are encountered in research and daily life and it is often of interest to compare these lists even when they are incomplete or have only some members in common. An example is document rankings returned for the same query by different search engines. A measure of the similarity between incomplete rankings should handle nonconjointness, weight high ranks more heavily than low, and be monotonic with increasing depth of evaluation; but no measure satisfying all these criteria currently exists. In this article, we propose a new measure having these qualities, namely rank-biased overlap (RBO). The RBO measure is based on a simple probabilistic user model. It provides monotonicity by calculating, at a given depth of evaluation, a base score that is non-decreasing with additional evaluation, and a maximum score that is nonincreasing. An extrapolated score can be calculated between these bounds if a point estimate is required. RBO has a parameter which determines the strength of the weighting to top ranks. We extend RBO to handle tied ranks and rankings of different lengths. Finally, we give examples of the use of the measure in comparing the results produced by public search engines and in assessing retrieval systems in the laboratory.

562 citations

Book
03 Jun 2010
TL;DR: This tutorial and review shows that despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research.
Abstract: Use of test collections and evaluation measures to assess the effectiveness of information retrieval systems has its origins in work dating back to the early 1950s. Across the nearly 60 years since that work started, use of test collections is a de facto standard of evaluation. This monograph surveys the research conducted and explains the methods and measures devised for evaluation of retrieval systems, including a detailed look at the use of statistical significance testing in retrieval experimentation. This monograph reviews more recent examinations of the validity of the test collection approach and evaluation measures as well as outlining trends in current research exploiting query logs and live labs. At its core, the modern-day test collection is little different from the structures that the pioneering researchers in the 1950s and 1960s conceived of. This tutorial and review shows that despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research.

383 citations