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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Bibliographic Details
Published in:Pattern recognition Vol. 47; no. 7; pp. 2532 - 2541
Main Authors: Liu, Zhun-ga, Pan, Quan, Dezert, Jean, Mercier, Grégoire
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.07.2014
Elsevier
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2014.01.011