http://ieeexplore.ieee.org TOC Alert for Publication# 5326 2009 June 19 39 4 C1 C1 41 39 4 C2 C2 37 39 4 377 378 50 The quality of data is an important issue in any domain-specific data mining and knowledge discovery initiative. The validity of solutions produced by data-driven algorithms can be diminished if the data being analyzed are of low quality. The quality of data is often realized in terms of data noise present in the given dataset and can include noisy attributes or labeling errors. Hence, tools for improving the quality of data are important to the data mining analyst. We present a comprehensive empirical investigation of our new and innovative technique for ranking attributes in a given dataset from most to least noisy. Upon identifying the noisy attributes, specific treatments can be applied depending on how the data are to be used. In a classification setting, for example, if the class label is determined to contain the most noise, processes to cleanse this important attribute may be undertaken. Independent variables or predictors that have a low correlation to the class attribute and appear noisy may be eliminated from the analysis. Several case studies using both real-world and synthetic datasets are presented in this study. The noise detection performance is evaluated by injecting noise into multiple attributes at different noise levels. The empirical results demonstrate conclusively that our technique provides a very accurate and useful ranking of noisy attributes in a given dataset. ]]> 39 4 379 388 317 Most of the intelligent knowledge-based applications contain components for measuring semantic similarity between terms. Many of the existing semantic similarity measures that use ontology structure as their primary source cannot measure semantic similarity between terms and concepts using multiple ontologies. This research explores a new way to measure semantic similarity between biomedical concepts using multiple ontologies. We propose a new ontology-structure-based technique for measuring semantic similarity in single ontology and across multiple ontologies in the biomedical domain within the framework of Unified Medical Language System (UMLS). The proposed measure is based on three features: 1) cross-modified path length between two concepts; 2) a new feature of common specificity of concepts in the ontology; and 3) local granularity of ontology clusters. The proposed technique was evaluated relative to human similarity scores and compared with other existing measures using two terminologies within UMLS framework: Medical Subject Headings and Systemized Nomenclature of Medicine Clinical Term. The experimental results validate the efficiency of the proposed technique in single and multiple ontologies, and demonstrate that our proposed measure achieves the best results of correlation with human scores in all experiments. ]]> 39 4 389 398 419 This paper deals with background knowledge in knowledge extraction from binary data. A background knowledge represents an additional piece of information a user may have along with the input data. Such information can be considered as specifying the type of knowledge a user is looking for in the data. In particular, we emphasize the need for taking into account background knowledge in formal concept analysis. We present an approach to modeling background knowledge that represents user's priorities regarding attributes and their relative importance. Such priorities serve as a constraint—only those formal concepts that are compatible with user's priorities are considered relevant, extracted from data, and presented to the user. Our approach has two main practical features. First, the number of formal concepts presented to the user may get significantly reduced. As a result, the user is supplied with relevant formal concepts only and is not overloaded by a large number of possibly nonrelevant formal concepts. Second, different priorities lead to different pieces of knowledge extracted from data. This way, the input data may be repeatedly used in knowledge extraction for different purposes corresponding to different priorities. We concentrate on foundational aspects such as mathematical feasibility, reasoning with background knowledge, removing redundancy from background knowledge, and computational tractability, and present several illustrative examples. In addition, we discuss directions for future research. ]]> 39 4 399 409 553 Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both MATLAB and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved. ]]> 39 4 410 419 825 Identification of the correct number of clusters is an important consideration in clustering where several cluster validity indexes, primarily utilizing the Euclidean distance, have been used in the literature. The property of symmetry is observed in most clustering solutions. In this paper, the symmetry versions of nine cluster validity indexes, namely, Davies–Bouldin index, Dunn index, generalized Dunn index, point symmetry (PS) index, $I$ index, Xie–Beni index, FS index, $K$ index, and SV index, are proposed. It is empirically established that incorporation of the property of symmetry significantly improves the capabilities of these indexes in identifying the appropriate number of clusters. A recently developed PS-based genetic clustering technique, GAPS clustering, is used as the underlying partitioning algorithm. Results on six artificially generated and five real-life datasets show that symmetry-distance-based $I$ index performs the best as compared to all the other eight indexes. ]]> 39 4 420 425 438 Marble quality classification is an important procedure generally performed by human experts. However, using human experts for classification is error prone and subjective. Therefore, automatic and computerized methods are needed in order to obtain reproducible and objective results. Although several methods are proposed for this purpose, we demonstrate that their performance is limited when dealing with diverse datasets containing a large number of quality groups. In this work, we test several feature sets and neural network topologies to obtain a better classification performance. During these tests, it is observed that different feature sets represent different subgroup(s) in a quality group rather than representing the whole group. Therefore, our approach is to use these features in a cascaded manner in which a quality group is classified by classifying all of its subgroups. We first realize this approach by using a two-stage cascaded network. Then, we design a hierarchical radial basis function network (HRBFN) in which correctly classified marble samples are taken out of the dataset and a different feature extraction method is applied to the remaining samples at each network level. The HRBFN system produces successful results for industrial applications and facilitates the desirable property of implementation in a quasi real-time manner. ]]> 39 4 426 439 1408 There is a fair amount of research about privacy, but few empirical studies about its cost have been conducted. In the area of secure multiparty computation, the scalar product has long been reckoned as one of the most promising alternatives to classic logic gates. The reason for this is that the scalar product is not only complete, which is as good as logic gates, but also much more efficient than logic gates. As a result, we set out to study the computation and communication resources needed for some of the most well-known and frequently referenced secure scalar product protocols, including the composite residuosity, the invertible matrix, the polynomial sharing, and the commodity-based approaches. In addition to the implementation details of these approaches, we analyze and compare their execution time, computation time, and memory and random number consumption. Moreover, Fairplay, the benchmark approach that implements Yao's circuit evaluation protocol, is also included in our experiments in order to demonstrate the potential for the scalar products to replace logic gates. ]]> 39 4 440 447 361 Strategy diffusion is a common phenomenon in the collective motion of multiagents, which is the large scale of strategy penetrations of certain agents on other agents; there are many kinds of diffusion forms; among them, the collective diffusion is always seen, which implies that a social strategy accepted by collective agents may have strong authority and tend to diffuse to other agents. This paper presents a novel spatial model for the collective strategy diffusion in multiagent societies. In the model, the social distance between agents can be measured in a euclidian space; the authority of a social strategy is determined by not only the number but also the collective social positions of its overlaid agents; social strategies that have strong authorities are impressed on the other agents, and the agents will accept (partially or in full) or reject them based on their own social strategies and social positions. Moreover, the paper also considers the concurrent form in the collective diffusion and presents that an agent's social strategy is influenced not only by the diffusion that bears on itself but also by other concurrent diffusion processes that bear on other agents, and an agent will incline to the average social strategy of the whole system, which can make the system more unified. Finally, the paper uses queue orientation as a case to study the presented model. ]]> 39 4 448 458 774 This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables. The proposed MODE learning algorithm adopts an evolutionary learning method to optimize the controller parameters. For design optimization, a new criterion is introduced. A hardware-in-the loop control technique is developed and applied to the designed ANFN controller using the MODE learning algorithm. The proposed ANFN controller with the MODE learning algorithm (ANFN-MODE) is used in two practical applications—the planetary-train-type inverted pendulum system and the magnetic levitation system. The experiment is developed in a real-time visual simulation environment. Experimental results of this study have demonstrated the robustness and effectiveness of the proposed ANFN-MODE controller. ]]> 39 4 459 473 558 Process mining techniques attempt to extract nontrivial and useful information from event logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities. Several successful case studies have been reported in literature, all demonstrating the applicability of process mining. However, these case studies refer to rather structured administrative processes. In this paper, we investigate the applicability of process mining to less structured processes. We report on a case study where the process mining (ProM) framework has been applied to the test processes of ASML (the leading manufacturer of wafer scanners in the world).This case study provides many interesting insights. On the one hand, process mining is also applicable to the less structured processes of ASML. On the other hand, the case study also shows the need for alternative mining approaches. ]]> 39 4 474 479 458 The early detection of faulty batteries is a critical work in the manufacturing processes of a secondary rechargeable battery. Conventional approaches use original performance degradation profiles of remaining capacity after recharge in order to detect faulty batteries. However, original degradation profiles with right-truncated test duration may not be effective in detecting faulty batteries. In this correspondence, we propose dual features functional support vector machine approach that uses both first and second derivatives of degradation profiles for early detection of faulty batteries with the reduced error rate. The modified floating search algorithm for the repeated feature selection with newly added degradation path points is presented to find a few good features for the enhanced detection while reducing the computation time for online implementation. After that, an attribute sampling plan considering time-varying classification errors is presented to determine the optimal number of test cycles and sample sizes by minimizing our proposed cost function. The real-life case study is presented to illustrate the proposed methodology and show its improved performance compared to existing approaches. The proposed method can be applied in a wide range of manufacturing processes to assess time-dependent quality characteristics. ]]> 39 4 480 485 369 39 4 486 486 149 39 4 487 487 151 39 4 488 488 370 39 4 C3 C3 29 39 4 C4 C4 36