On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Shafer-dempster reasoning with applications to multisensor target identification systems
IEEE Transactions on Systems, Man and Cybernetics
A comparison of two evidential reasoning schemes
Artificial Intelligence
Elements of information theory
Elements of information theory
“Change-glasses” approach in pattern recognition
Pattern Recognition Letters
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification by fuzzy integral: performance and tests
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Methods for combining experts' probability assessments
Neural Computation
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Fuzzy logic detection of landmines with ground penetrating radar
Signal Processing - Special issue on fuzzy logic in signal processing
Evaluating strategies and systems for content based indexing of person images on the Web
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Sensor and Data Fusion Concepts and Applications
Sensor and Data Fusion Concepts and Applications
Decision Level Fusion of Intramodal Personal Identity Verification Experts
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Fusion of multiple classifiers for intrusion detection in computer networks
Pattern Recognition Letters
Sensor data fusion for context-aware computing using dempster-shafer theory
Sensor data fusion for context-aware computing using dempster-shafer theory
EURASIP Journal on Applied Signal Processing
Neural network combination by fuzzy integral for robust change detection in remotely sensed imagery
EURASIP Journal on Applied Signal Processing
Information fusion and sparsity promotion using choquet integrals
Information fusion and sparsity promotion using choquet integrals
Clustering by competitive agglomeration
Pattern Recognition
Sensor fusion in anti-personnel mine detection using a two-level belief function model
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid fuzzy-neural systems in handwritten word recognition
IEEE Transactions on Fuzzy Systems
Classification by nonlinear integral projections
IEEE Transactions on Fuzzy Systems
Minimum Classification Error Training for Choquet Integrals With Applications to Landmine Detection
IEEE Transactions on Fuzzy Systems
Detection of mines and minelike targets using principal component and neural-network methods
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)
Expert Systems with Applications: An International Journal
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Many algorithms have been proposed for detecting anti-tank landmines and discriminating between mines and clutter objects using data generated by a ground penetrating radar (GPR) sensor. Our extensive testing of some of these algorithms has indicated that their performances are strongly dependent upon a variety of factors that are correlated with geographical and environmental conditions. It is typically the case that one algorithm may perform well in one setting and not so well in another. Thus, fusion methods that take advantage of the stronger algorithms for a given setting without suffering from the effects of weaker algorithms in the same setting are needed to improve the robustness of the detection system. In this paper, we discuss, test, and compare seven different fusion methods: Bayesian, distance-based, Dempster-Shafer, Borda count, decision template, Choquet integral, and context-dependent fusion. We present the results of a cross validation experiment that uses a diverse data set together with results of eight detection and discrimination algorithms. These algorithms are the top ranked algorithms after extensive testing. The data set was acquired from multiple collections from four outdoor sites at different locations using the NIITEK GPR system. This collection covers over 41,807m^2 of ground and includes 1593 anti-tank mine encounters.