Credal classification rule for uncertain data based on belief functions

In this paper we present a new credal classification rule (CCR) based on belief functions to deal with the uncertain data. CCR allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the...

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Vydáno v:	Pattern recognition Ročník 47; číslo 7; s. 2532 - 2541
Hlavní autoři:	Liu, Zhun-ga, Pan, Quan, Dezert, Jean, Mercier, Grégoire
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 01.07.2014 Elsevier
Témata:	Applied sciences Artificial intelligence Belief functions Classification Computation Computer science; control theory; systems Credal classification Data classification Data processing. List processing. Character string processing Engineering Sciences Evidence theory Exact sciences and technology Information theory Information, signal and communications theory Learning and adaptive systems Mathematical analysis Mathematical models Memory organisation. Data processing Noise Pattern recognition Prototypes Signal and Image processing Software Telecommunications and information theory Uncertain data Credal classification Data classification Belief functions Evidence theory Uncertain data Belief Data analysis Statistical analysis Prototype Expert system Knowledge base Multivariate analysis Indistinguishability Outlier Disjunction Uncertain system Classification Dempster Shafer theory Metric Proof theory Mahalanobis distance
ISSN:	0031-3203, 1873-5142
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Popis
Shrnutí:	In this paper we present a new credal classification rule (CCR) based on belief functions to deal with the uncertain data. CCR allows the objects to belong (with different masses of belief) not only to the specific classes, but also to the sets of classes called meta-classes which correspond to the disjunction of several specific classes. Each specific class is characterized by a class center (i.e. prototype), and consists of all the objects that are sufficiently close to the center. The belief of the assignment of a given object to classify with a specific class is determined from the Mahalanobis distance between the object and the center of the corresponding class. The meta-classes are used to capture the imprecision in the classification of the objects when they are difficult to correctly classify because of the poor quality of available attributes. The selection of meta-classes depends on the application and the context, and a measure of the degree of indistinguishability between classes is introduced. In this new CCR approach, the objects assigned to a meta-class should be close to the center of this meta-class having similar distances to all the involved specific classes׳ centers, and the objects too far from the others will be considered as outliers (noise). CCR provides robust credal classification results with a relatively low computational burden. Several experiments using both artificial and real data sets are presented at the end of this paper to evaluate and compare the performances of this CCR method with respect to other classification methods. •A new credal classification rule (CCR) is proposed to deal with uncertain data.•A justified use of meta-class is introduced to model the imprecision of data.•A new method is presented for the determination of bba׳s of the object.•The computation burden of CCR is quite low.•The experiments with real data are given to illustrate the interest of CCR.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0031-3203 1873-5142
DOI:	10.1016/j.patcog.2014.01.011