Information combination operators for data fusion: a comparative review with classification
01 Jan 1996-Vol. 26, Iss: 1, pp 52-67
TL;DR: A classification of operators issued from the different data fusion theories with respect to their behavior provides a guide for choosing an operator in a given problem and can be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictive situations.
Abstract: In most data fusion systems, the information extracted from each sensor (either numerical or symbolic) is represented as a degree of belief in an event with real values, taking in this way into account the imprecise, uncertain, and incomplete nature of the information. The combination of such degrees of belief is performed through numerical fusion operators. A very large variety of such operators has been proposed in the literature. We propose in this paper a classification of these operators issued from the different data fusion theories with respect to their behavior. Three classes are thus defined. This classification provides a guide for choosing an operator in a given problem. This choice can then be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictive situations.
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TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.
2,741 citations
Cites methods from "Information combination operators f..."
...Different approaches have been used to derive a soft classifier, including fuzzy-set theory, Dempster–Shafer theory, certainty factor (Bloch 1996), softening the output of a hard classification from maximum likelihood (Schowengerdt 1996), IMAGINE’s subpixel classifier (Huguenin et al. 1997), and…...
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...Different approaches have been used to derive a soft classifier, including fuzzy‐set theory, Dempster–Shafer theory, certainty factor (Bloch 1996 ), softening the output of a hard classification from maximum likelihood (Schowengerdt 1996 ), IMAGINE's subpixel classifier (Huguenin et al....
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TL;DR: Conditions under which ensemble based systems may be more beneficial than their single classifier counterparts are reviewed, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined are reviewed.
Abstract: In matters of great importance that have financial, medical, social, or other implications, we often seek a second opinion before making a decision, sometimes a third, and sometimes many more. In doing so, we weigh the individual opinions, and combine them through some thought process to reach a final decision that is presumably the most informed one. The process of consulting "several experts" before making a final decision is perhaps second nature to us; yet, the extensive benefits of such a process in automated decision making applications have only recently been discovered by computational intelligence community. Also known under various other names, such as multiple classifier systems, committee of classifiers, or mixture of experts, ensemble based systems have shown to produce favorable results compared to those of single-expert systems for a broad range of applications and under a variety of scenarios. Design, implementation and application of such systems are the main topics of this article. Specifically, this paper reviews conditions under which ensemble based systems may be more beneficial than their single classifier counterparts, algorithms for generating individual components of the ensemble systems, and various procedures through which the individual classifiers can be combined. We discuss popular ensemble based algorithms, such as bagging, boosting, AdaBoost, stacked generalization, and hierarchical mixture of experts; as well as commonly used combination rules, including algebraic combination of outputs, voting based techniques, behavior knowledge space, and decision templates. Finally, we look at current and future research directions for novel applications of ensemble systems. Such applications include incremental learning, data fusion, feature selection, learning with missing features, confidence estimation, and error correcting output codes; all areas in which ensemble systems have shown great promise
2,628 citations
Cites background from "Information combination operators f..."
...The long list includes composite classifier systems [1], mixture of experts [4], [5], stacked generalization [6], consensus aggregation [7], combination of multiple classifiers [8]–[12], change-glasses approach to classifier selection [13], dynamic classifier selection [12], classifier fusion [14]–[16], committees of neural networks [17], voting pool of classifiers [18], classifier ensembles [17], [19], and pandemonium system of reflective agents [20], among many others....
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TL;DR: A comprehensive review of the data fusion state of the art is proposed, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies.
1,684 citations
TL;DR: This tutorial performs a synthesis between the multiscale-decomposition-based image approach, the ARSIS concept, and a multisensor scheme based on wavelet decomposition, i.e. a multiresolution image fusion approach.
1,187 citations
Cites background from "Information combination operators f..."
...Sometimes the degree of belief in a given event can be used to determine the di4erent weights [26]....
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01 Jan 2001
TL;DR: This work presents here a simple rule for adapting the class combiner to the application and shows that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets.
Abstract: Multiple classi"er fusion may generate more accurate classi"cation than each of the constituent classi"ers. Fusion is often based on "xed combination rules like the product and average. Only under strict probabilistic conditions can these rules be justi"ed. We present here a simple rule for adapting the class combiner to the application. c decision templates (one per class) are estimated with the same training set that is used for the set of classi"ers. These templates are then matched to the decision pro"le of new incoming objects by some similarity measure. We compare 11 versions of our model with 14 other techniques for classi"er fusion on the Satimage and Phoneme datasets from the database ELENA. Our results show that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
968 citations
References
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01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
52,705 citations
03 Jan 1988
TL;DR: A type of operator for aggregation called an ordered weighted aggregation (OWA) operator is introduced and its performance is found to be between those obtained using the AND operator and the OR operator.
Abstract: The author is primarily concerned with the problem of aggregating multicriteria to form an overall decision function. He introduces a type of operator for aggregation called an ordered weighted aggregation (OWA) operator and investigates the properties of this operator. The OWA's performance is found to be between those obtained using the AND operator, which requires all criteria to be satisfied, and the OR operator, which requires at least one criteria to be satisfied. >
6,534 citations
TL;DR: In this paper, a quantification scheme is proposed to model the inexact reasoning processes of medical experts, which is essentially an approximation to conditional probability, but offers advantages over Bayesian analysis when they are utilized in a rule-based computer diagnostic system.
Abstract: Medical science often suffers from having so few data and so much imperfect knowledge that a rigorous probabilistic analysis, the ideal standard by which to judge the rationality of a physician's decision, is seldom possible Physicians nevertheless seem to have developed an ill-defined mechanism for reaching decisions despite a lack of formal knowledge regarding the interrelationships of all the variables that they are considering This report proposes a quantification scheme which attempts to model the inexact reasoning processes of medical experts The numerical conventions provide what is essentially an approximation to conditional probability, but offer advantages over Bayesian analysis when they are utilized in a rule-based computer diagnostic system One such system, a clinical consultation program named mycin , is described in the context of the proposed model of inexact reasoning
1,299 citations
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1,286 citations