Intelligent Data Analysis: An International Journal

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.

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.

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.

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.

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.