Intelligent Data Analysis - Volume 14, issue 4

Authors: Ho, Tu Bao | Zhou, Zhi-Hua | Motoda, Hiroshi

Article Type: Editorial

DOI: 10.3233/IDA-2010-0430

Citation: Intelligent Data Analysis, vol. 14, no. 4, pp. 437-438, 2010

Authors: Cuzzolin, Fabio

Article Type: Research Article

Abstract: In this paper we introduce three alternative combinatorial formulations of the theory of evidence (ToE), by proving that both plausibility and commonality functions share the structure of “sum function” with belief functions. We compute their Moebius inverses, which we call basic plausibility and commonality assignments. In the framework of the geometric approach to uncertainty measures the equivalence of the associated formulations of the ToE is mirrored by the geometric congruence of the related simplices. We can therefore describe the point-wise geometry of these sum functions in terms of rigid transformations mapping them onto each other. Combination rules can be applied …to plausibility and commonality functions through their Moebius inverses, leading to interesting applications of such inverses to the probabilistic transformation problem. Show more

Keywords: Theory of evidence, combinatorics, sum function, Moebius inverse, basic plausibility and commonality assignment, convex geometry, simplex, congruence

DOI: 10.3233/IDA-2010-0431

Citation: Intelligent Data Analysis, vol. 14, no. 4, pp. 439-464, 2010

Authors: Murayama, Norifumi | Okumura, Manabu

Article Type: Research Article

Abstract: We present a new approach to detect abbreviations given a root expression. The method is based on a statistical model combining two internal models: a generation and a verification model. The statistical model accounts for both the validity of abbreviations as a character sequence generated from a root (as learnt from the collection of abbreviation-root pairs) and their social validity, indicating how they are really used in the world (as obtained from a web search engine). The experimental results showed that our method outperforms traditional template-based methods. Specifically, using co-occurrence in the verification model yielded the best performance in our …method. Show more

Keywords: Japanese abbreviation, statistical model, generation model

DOI: 10.3233/IDA-2010-0432

Citation: Intelligent Data Analysis, vol. 14, no. 4, pp. 465-478, 2010

Authors: Sun, Jun | Zhao, Wenbo | Xue, Jiangwei | Shen, Zhiyong | Shen, Yidong

Article Type: Research Article

Abstract: We propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature s is more important than feature t. Our clustering formulation aims to incorporate feature order preferences into prototype-based clustering. The derived algorithm automatically learns distortion measures parameterized by feature weights which will respect the feature order preferences as much as possible. Our method allows the use of a broad range of distortion measures such as Bregman divergences. Moreover, even when generalized entropy is used in the regularization term, the subproblem of learning the feature weights is still a convex programming problem. Empirical results …on some datasets demonstrate the effectiveness and potential of our method. Show more

Keywords: Clustering, domain knowledge, Bregman divergence, feature order preferences, entropy regularization, prototype-based clustering, convex optimization, quadratic programming

DOI: 10.3233/IDA-2010-0433

Citation: Intelligent Data Analysis, vol. 14, no. 4, pp. 479-495, 2010

Authors: Hashimoto, Takashi | Warashina, Katsuhide | Yamauchi, Hajime

Article Type: Research Article

Abstract: This paper studies the characteristics of a new composite evolutionary computation algorithm in which genetic evolution, individual learning and social learning interact in NK fitness landscape. We derive conditions for effective social learning in static and dynamic environments using computer simulations of a model of the composite evolutionary algorithm. The conditions for static environments are: the individual learning cost should be at least 1.5 times than the social one; the mutation rate should be less than 0.04 per each gene; more than 3 genes should not interact. These conditions qualitatively mean that: the individual learning cost is larger than the …social learning cost; teaching is beneficial for teachers; mutation rate is not too high, must be smaller than error thresold; the fitness landscape is not so complex. We also show that this algorithm is effective in dynamic environments in which NK fitness landscape changes with time, if these conditions are satisfied. Frequent environmental change favors social learning, but under more severe conditions, such as high epistasis and higher mutation rate than the error threshold, individual learning is more useful in finding better solutions. Show more

Keywords: Evolutionary computation, Genetic evolution, Individual learning, Social learning, NK fitness landscape, Dynamic environment

DOI: 10.3233/IDA-2010-0434

Citation: Intelligent Data Analysis, vol. 14, no. 4, pp. 497-514, 2010

Authors: Ueta, Atsushi | Yairi, Takehisa | Kanazaki, Hirofumi | Machida, Kazuo

Article Type: Research Article

Abstract: This paper proposes an alternative solution to a mapping problem in two different cases; when bearing measurements to features (landmarks) and odometry are measured and when bearing and range measurements to features are measured. Our approach named M-SEIFD (Mapping by Sequential Estimation of Inter-Feature Distances) first estimates inter-feature distances, then finds global position of all the features by enhanced multi-dimensional scaling (MDS). M-SEIFD is different from the conventional SLAM methods based on Bayesian filtering in that robot self-localization is not compulsory and that M-SEIFD is able to utilize prior information about relative distances among features directly. We show that M-SEIFD …is able to achieve a decent map of features both in simulation and in real-world environment with a mobile robot. Show more

Keywords: Mapping, mobile robot, multi-dimensional scaling

DOI: 10.3233/IDA-2010-0435

Citation: Intelligent Data Analysis, vol. 14, no. 4, pp. 515-529, 2010