This paper proposes an idea of approximating the hypervolume of a non-dominated solution set using a number of achievement scalarizing functions with uniformly distributed weight vectors. Each achievement scalarizing ...
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This paper proposes an idea of approximating the hypervolume of a non-dominated solution set using a number of achievement scalarizing functions with uniformly distributed weight vectors. Each achievement scalarizing function with a different weight vector is used to measure the distance from the reference point of the hypervolume to the attainment surface of the non-dominated solution set along its own search direction specified by its weight vector. Our idea is to approximate the hypervolume by the average distance from the reference point to the attainment surface over a large number of uniformly distributed weight vectors (i.e., over various search directions). We examine the effect of the number of weight vectors (i.e., the number of search directions) on the approximation accuracy and the computation time of the proposed approach. As expected, experimental results show that the approximation accuracy is improved by increasing the number of weight vectors. It is also shown that the proposed approach needs much less computation time than the exact hypervolume calculation for a six-objective knapsack problem even when we use about 100,000 weight vectors.
This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model ...
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This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster principal coordinate analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of principal coordinate analysis considering fuzzy membership degrees to the cluster.
Cellular evolutionary algorithms usually use a single neighborhood structure for local selection. When a new solution is to be generated by crossover and/or mutation for a cell, a pair of parent solutions is selected ...
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Cellular evolutionary algorithms usually use a single neighborhood structure for local selection. When a new solution is to be generated by crossover and/or mutation for a cell, a pair of parent solutions is selected from its neighbors. The current solution at the cell is replaced with the newly generated offspring if the offspring has the higher fitness value than the current one. That is, the ldquoreplace-if-betterrdquo policy is used for the replacement of the current solution. Local selection, crossover, mutation and replacement are iterated at every cell in cellular algorithms. A recently proposed multiobjective evolutionary algorithm called MOEA/D by Zhang and Li (2007) can be viewed as a cellular algorithm where each cell has its own scalarizing fitness function with a different weight vector. We can introduce a spatial structure to MOEA/D by the Euclidean distance between weight vectors. Its main difference from standard cellular algorithms is that a newly generated offspring for a cell is compared with not only the current solution of the cell but also its neighbors for local replacement in MOEA/D. In this paper, we examine the effect of local replacement on the search ability of a cellular version of MOEA/D. Whereas the same neighborhood structure was used for local selection and local replacement in the original MOEA/D, we examine the use of different neighborhood structures for local selection and local replacement. It is shown through computational experiments on multiobjective 0/1 knapsack problems with two, four and six objectives that local replacement plays an important role in MOEA/D especially for many-objective optimization problems.
This paper reports on the performance of the fuzzy c-means based classifier (FCMC). Test set performances optimized by way of several CV procedures and three sets of hyperparameters are throughly compared. UCI benchma...
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This paper reports on the performance of the fuzzy c-means based classifier (FCMC). Test set performances optimized by way of several CV procedures and three sets of hyperparameters are throughly compared. UCI benchmark datasets are used to evaluate the performance. FCM classifier in combination with standard 10-CV procedure or resubstitution (i.e., 1-CV) procedure for parameter selection achieves good test set performance compared to k-nearest neighbor classifier (k-NN). Randomized test sets performance of the classifier is comparable to that of the support vector machine (SVM) reported in the literature.
This paper proposes an additional version of the fuzzy c-means based classifier (FCMC). The classifier FCMC-R treats relational data instead of object data. FCMCs use covariance structures to represent flexible shapes...
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This paper proposes an additional version of the fuzzy c-means based classifier (FCMC). The classifier FCMC-R treats relational data instead of object data. FCMCs use covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional feature data. In order to tackle with this problem, we proposed a way of directly handling high-dimensional data, i.e., FCMC-H. The third type of the FCM classifier is the relational classifier FCMC-R, which is derived from FCMC-H. The relational data represented by a relational matrix are based on dissimilarities or distances between object data. The triplets, i.e., FCMC, FCMC-H, and FCMC-R are equivalent when the dimensionality of feature vectors is not very high and the dissimilarity is represented by Euclidean distances. The randomized test set performance of FCMC on the sets of object data from UCI repository is comparable to that of the support vector machine (SVM) classifier. The performances of the triplet in terms of 100 times three way data splits (3-WDS) procedure are compared. The triplet surpasses the k-nearest neighbor (k-NN) classifier, which is a well established and very popular relational classifier.
This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized re...
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This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized recommendation is performed by finding user-item neighborhoods (co-clusters) from a rectangular relational data matrix, in which users and items have mutually positive relations. In the proposed approach, user-item clusters are extracted one by one in a sequential manner via a structural balancing technique, used in conjunction with the sequential fuzzy cluster extraction method.
A conceptual graph generation method is proposed in this paper. A conceptual graph is useful for studying human verbal caring interactions such as counseling, based on an interpersonal psychological approach referred ...
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A conceptual graph generation method is proposed in this paper. A conceptual graph is useful for studying human verbal caring interactions such as counseling, based on an interpersonal psychological approach referred to as 'naive psychology'. We apply the visual assessment of clustering tendency (VAT) to naive psychology, with particular reference to the visual understanding of people. A conceptual graph is constructed from words and sentences selected by morphological analysis. Furthermore, the VAT algorithm produces a visual display that can be used to assess clustering tendencies in a set of persons (notions) by reconstructing a digital image representation of a square relational dissimilarity matrix. This algorithm clearly represents two types of imbalanced situations in naive psychology: namely the crisp and fuzzy situations. In addition, social simulations that utilize several graphs are introduced.
In cyber-physical systems, which are the integrations of computational and physical processes, it is hard to realize certain security properties. Fundamentally, physically observable behavior leads to violations of co...
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In cyber-physical systems, which are the integrations of computational and physical processes, it is hard to realize certain security properties. Fundamentally, physically observable behavior leads to violations of confidentiality. We focus on analyzing certain noninterference based security properties to ensure that interactions between the cyber and physical processes preserve confidentiality. A considerable barrier to this analysis is representing the physical systempsilas interactions. In this paper, these physical system properties are encoded into a discrete event system and the combined cyber-physical system is described using the process algebra SPA. The model checker, CoPS shows BNDC (bisimulation based non deducibility on compositions) properties,which are a variant of noninterference properties, to check the systempsilas security against all high level potential interactions. We consider a model problem of invariant pipeline flow to examine the BNDC properties and their applicability for cyber-physical systems.
We report preliminary data of an initial laboratory study examining the effectiveness of self-regulated learning (SRL) training versus no training on learners' ability to deploy SRL processes and learn about the c...
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Information security requires a method to establish digital credentials that can reliably identify individual users. Since biometrics is concerned with the measurements of unique human physiological or behavioural cha...
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ISBN:
(纸本)9781424427734
Information security requires a method to establish digital credentials that can reliably identify individual users. Since biometrics is concerned with the measurements of unique human physiological or behavioural characteristics, the technology has been used to verify the identity of computer or network users. Given today's heightened security requirements of military as well as other applications such as banking, health care, etc., it is becoming critical to be able to monitor the presence of the authenticated user throughout a session. This paper presents a prototype system that uses facial recognition technology to monitor the authenticated user. The objective is to ensure that the user who is using the computer is the same person that logged onto the system. A neural network-based algorithm is implemented to carry out face detection, and an eigenface method is employed to perform facial recognition. A graphical user interface (GUI) has been developed which allows the performance of face detection and facial recognition to be monitored at run time. The experimental results demonstrate the feasibility of near-real-time continuous user verification for high-level security information systems.
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