A Ranking Distance Based Diversity Measure for Multiple Classifier Systems

International audienceMultiple classifier fusion belongs to the decision-level information fusion, which has been widely used in many pattern classification applications, especially when the single classifier is not competent. However, multiple classifier fusion can not assure the improvement of the classification accuracy. The diversity among those classifiers in the multiple classifier system (MCS) is crucial for improving the fused classification accuracy. Various diversity measures for MCS have been proposed, which are mainly based on the average sample-wise classification consistency between different member classifiers. In this paper, we propose to define the diversity between member classifiers from a different standpoint. If different member classifiers in an MCS are good at classifying different classes, i.e., there exist expert-classifiers for each concerned class, the improvement of the accuracy of classifier fusion can be expected. Each classifier has a ranking of classes in term of the classification accuracies, based on which, a new diversity measure is implemented using the ranking distance. A larger average ranking distance represents a higher diversity. The new proposed diversity measure is used together with each single classifier's performance on training samples to design and optimize the MCS. Experiments, simulations , and related analyses are provided to illustrate and validate our new proposed diversity measure

Sur la décombinaison de fonctions de croyance

International audienceThe evidence combination is a kind of decision-level information fusion in the theory of belief functions. Given two basic belief assignments (BBAs) originated from different sources, one can combine them using some combination rule, e.g., Dempster's rule to expect a better decision result. If one only has a combined BBA, how to determine the original two BBAs to combine? This can be considered as a defusion of information. This is useful, e.g., one can analyze the difference or dissimilarity between two different information sources based on the BBAs obtained using evidence decombination. Therefore, in this paper, we research on such a defusion in the theory of belief functions. We find that it is a well-posed problem if one original BBA and the combined BBA are both available, and it is an under-determined problem if both BBAs to combine are unknown. We propose an optimization-based approach for the evidence decombination according to the criteria of divergence maximization. Numerical examples are provided illustrate and verify our proposed decombination approach, which is expected to be used in applications such the difference analysis between information sources in information fusion systems when the original BBAs are discarded, and performance evaluation of combination rules

Total belief theorem and conditional belief functions

In this paper new theoretical results for reasoning with belief functions are obtained and discussed. After a judicious decomposition of the set of focal elements of a belief function, we establish the Total Belief Theorem (TBT) which is the direct generalization of the Total Probability Theorem when working in the framework of belief functions. The TBT is also generalized for dealing with different frames of discernments thanks to Cartesian product space. From TBT, we can derive and define formally the expressions of conditional belief functions which are consistent with the bounds of imprecise conditional probability. This work provides a direct establishment and solid justification of Fagin-Halpern belief conditioning formulas. The well-known Bayes' Theorem of Probability Theory is then generalized in the framework of belief functions and we illustrate it with an example at the end of this paper

Une formulation simplifiée du théorème de Bayes généralisé

International audienceIn this paper we present a simple formulation of the Generalized Bayes' Theorem (GBT) which extends Bayes' theorem in the framework of belief functions. We also present the condition under which this new formulation is valid. We illustrate our theoretical results with simple examples

A new hierarchical ranking aggregation method

International audienceThe purpose of ranking aggregation (or fusion) is to combine multiple rankings to a consensus one. In the ranking aggregation, some of the items’ preference orders are easy to distinguish, however, some others’ are not. To specifically compare the ambiguous items, i.e., the items whose aggregated preference orders are difficult to distinguish, is helpful for ranking aggregation. In this paper, a new hierarchical ranking aggregation method is proposed. The items whose preference orders are easy to distinguish are first divided into different ranking levels (i.e., the ordered items subsets), and the ambiguous items are put into the same ranking level. The items in high ranking levels are ranked higher than the items in low ranking levels in the aggregated ranking. Then the items in the same ranking level are further compared and divided into multiple ranking sub-levels. The aggregated ranking is generated hierarchically by dividing the same ranking levels’ (or sub-levels’) items into sub-levels until each sub-level only includes one item. Furthermore, we discuss the way of using the insertion sort method for merging the adjacent levels’ rankings to improve the quality of the aggregated ranking. The experiments and simulations show that our new hierarchical methods perform well in ranking aggregation

Exploring the diversity and potential functional characteristics of microbiota associated with different compartments of Schisandra chinensis

IntroductionSymbiotic microbial have a significant impact on the growth and metabolism of medicinal plants. Schisandra chinensis is a very functionally rich medicinal herb; however, its microbial composition and diversity have been poorly studied.MethodsIn the present study, the core microbiomes associated with the rhizospheric soil, roots, stems, leaves, and fruits of S. chinensis from six geographic locations were analyzed by a macro-genomics approach.ResultsAlpha and beta diversity analyses showed that the diversity of microbial composition of S. chinensis fruits did not differ significantly among the geographic locations as compared to that in different plant compartments. Principal coordinate analysis showed that the microbial communities of S. chinensis fruits from the different ecological locations were both similar and independent. In all S. chinensis samples, Proteobacteria was the most dominant bacterial phylum, and Ascomycota and Basidiomycota were the most dominant fungal phyla. Nitrospira, Bradyrhizobium, Sphingomonas, and Pseudomonas were the marker bacterial populations in rhizospheric soils, roots, stems and leaves, and fruits, respectively, and Penicillium, Golubevia, and Cladosporium were the marker fungal populations in the rhizospheric soil and roots, stems and leaves, and fruits, respectively. Functional analyses showed a high abundance of the microbiota mainly in biosynthesis.DiscussionThe present study determined the fungal structure of the symbiotic microbiome of S. chinensis, which is crucial for improving the yield and quality of S. chinensis

A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures

Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC). Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency

New Basic belief assignment approximations based on optimization

International audienceThe theory of belief function, also called Dempster- Shafer evidence theory, has been proved to be a very useful representation scheme for expert and other knowledge based systems. However, the computational complexity of evidence combination will become large with the increasing of the frame of discernment's cardinality. To reduce the computational cost of evidence combination, the idea of basic belief assignment (bba) approximation was proposed, which can reduce the complexity of the given bba's. To realize a good bba approximation, the approximated bba should be similar (in some sense) to the original bba. In this paper, we use the distance of evidence together with the difference between the uncertainty degree of approximated bba and that of the original one to construct a comprehensive measure, which can represent the similarity between the approximated bba and the original one. By using such a comprehensive measure as the objective function and by designing some constraints, the bba approximation is converted to an optimization problem. Comparative experiments are provided to show the rationality of the construction of comprehensive similarity measure and that of the constraints designed