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Combining Predictions in Pairwise Classification: An Optimal Adaptive Voting Strategy and Its Relation to Weighted Voting
 TO APPEAR IN PATTERN RECOGNITION
, 2009
"... Weighted voting is the commonly used strategy for combining predictions in pairwise classification. Even though it shows good classification performance in practice, it is often criticized for lacking a sound theoretical justification. In this paper, we study the problem of combining predictions wit ..."
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Cited by 13 (0 self)
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Weighted voting is the commonly used strategy for combining predictions in pairwise classification. Even though it shows good classification performance in practice, it is often criticized for lacking a sound theoretical justification. In this paper, we study the problem of combining predictions within a formal framework of label ranking and, under some model assumptions, derive a generalized voting strategy in which predictions are properly adapted according to the strengths of the corresponding base classifiers. We call this strategy adaptive voting and show that it is optimal in the sense of yielding a MAP prediction of the class label of a test instance. Moreover, we offer a theoretical justification for weighted voting by showing that it yields a good approximation of the optimal adaptive voting prediction. This result is further corroborated by empirical evidence from experiments with real and synthetic data sets showing that, even though adaptive voting is sometimes able to achieve consistent improvements, weighted voting is in general quite competitive, all the more in cases where the aforementioned model assumptions underlying adaptive voting are not met. In this sense, weighted voting appears to be a more robust aggregation strategy.
FR3: A fuzzy rule learner for inducing reliable classifiers
 IEEE Transactions Fuzzy Systems
, 2009
"... This paper introduces a fuzzy rulebased classification method called FR3, which is short for ..."
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Cited by 12 (1 self)
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This paper introduces a fuzzy rulebased classification method called FR3, which is short for
Decision Support with Belief Functions Theory for Seabed Characterization
, 805
"... Abstract — The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one ..."
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Cited by 6 (6 self)
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Abstract — The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as oneversusone or oneversusrest, usually used in the SVMclassification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.
Extending stochastic ordering to belief functions on the real line
, 2010
"... In this paper, the concept of stochastic ordering is extended to belief functions on the real line defined by random closed intervals. In this context, the usual stochastic ordering is shown to break down into four distinct ordering relations, called credal orderings, which correspond to the four ba ..."
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In this paper, the concept of stochastic ordering is extended to belief functions on the real line defined by random closed intervals. In this context, the usual stochastic ordering is shown to break down into four distinct ordering relations, called credal orderings, which correspond to the four basic ordering structures between intervals. These orderings are characterized in terms of lower and upper expectations. We then derive the expressions of the least committed (least informative) belief function credally less (respectively, greater) than or equal to a given belief function. In each case, the solution is a consonant belief function that can be described by a possibility distribution. A simple application to reliability analysis is used as an example throughout the paper.
CUED SPEECH HAND SHAPE RECOGNITION Belief Functions as a Formalism to Fuse SVMs & Expert Systems
"... As part of our work on hand gesture interpretation, we present our results on hand shape recognition. Our method is based on attribute extraction and multiple binary SVM classification. The novelty lies in the fashion the fusion of all the partial classification results are performed. This fusion is ..."
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As part of our work on hand gesture interpretation, we present our results on hand shape recognition. Our method is based on attribute extraction and multiple binary SVM classification. The novelty lies in the fashion the fusion of all the partial classification results are performed. This fusion is (1) more efficient in terms of information theory and leads to more accurate result, (2) general enough to allow other source of information to be taken into account: Each SVM output is transformed to a belief function, and all the corresponding functions are fused together with some other external evidential sources of information. 1
8 Decision Support with Belief Functions Theory for Seabed Characterization
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FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers
"... This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the wellknown RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3 is ..."
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This paper introduces a fuzzy rulebased classification method called FR3, which is short for Fuzzy Round Robin RIPPER. In the context of polychotomous classification, it uses a fuzzy extension of the wellknown RIPPER algorithm as a base learner within a round robin scheme. A key feature of FR3 is its ability to represent different facets of uncertainty involved in a classification decision in a more faithful way, thereby providing the basis for implementing “reliable classifiers ” that may, for example, abstain from a decision when not being sure enough. 1
Refined classifier combination using belief functions
 11TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION ‘08), ALLEMAGNE
, 2008
"... We address here the problem of supervised classification using belief functions. In particular, we study the combination of nonindependent sources of information. In a companion paper [1], we showed that the cautious rule of combination [2], [3] may be best suited than the widely used Dempster’s R ..."
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We address here the problem of supervised classification using belief functions. In particular, we study the combination of nonindependent sources of information. In a companion paper [1], we showed that the cautious rule of combination [2], [3] may be best suited than the widely used Dempster’s Rule to combine classifiers in the case of real data. Then, we considered combination rules intermediate between the cautious rule and Dempster’s rule. We proposed a method for choosing the combination rule that optimizes the classification accuracy over a set of data. Eventually, we mentioned a generalized approach, in which a refined combination rule best suited to complex dependencies of the sources to combine is learnt. Here, we extensively study this latter approach. It consists in clustering the sources according to some measure of similarity; then, one rule is learnt for combining the sources within the clusters, and another for combining the results thus obtained. We conduct experiments on various real data sets that show the interest of this approach.