Pattern Recognition Letters
Recognizing and Filtering Web Images Based on People's Existence
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Evaluation of k-Nearest Neighbor classifier performance for direct marketing
Expert Systems with Applications: An International Journal
Evidential classifier for imprecise data based on belief functions
Knowledge-Based Systems
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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The paper presents a fuzzy evidential reasoning algorithmin light of the Dempster-Shafer evidence theory andthe K-nearest neighbor algorithm for pattern classification.Given an input pattern to be classified, each of its K nearestneighbors is viewed as an evidence source, in terms ofa fuzzy evidence structure. The distance between the inputpattern and each of its K nearest neighbors is usedfor mass determination while the contextual information ofthe nearest neighbor in the training sample space is formulatedby a fuzzy set in determining a fuzzy focal element.Therefore, pooling evidence provided by neighbors is realizedby a fuzzy evidential reasoning, where feature selectionis further considered through ranking and adaptive combinationof neighbors. A fast implementation scheme of thefuzzy evidential reasoning is also developed. Experimentalresults of classifying multi-channel remote sensing imageshave shown that the proposed approach outperforms the K-nearestneighbor (K-NN) algorithm [1], the fuzzy K-nearestneighbor (F-KNN) algorithm [2], the evidence-theoretic K-nearestneighbor (E-KNN) algorithm [3], and the fuzzy ex-tendedversion of E-KNN (FE-KNN) [4], in terms of theclassification accuracy and insensitivity to the number Kof nearest neighbors.