Using the generalized fuzzy k-nearest neighbor classifier for biomass feedstocks classification
Mahinda Mailagaha Kumbure, Pasi Luukka
Citation: Recent Advances in Business Analytics. Selected papers of the 2021 KNOWCON-NSAIS workshop on Business Analytics, Jan Stoklasa, Pasi Luukka and Maria Ganzha (eds). ACSIS, Vol. 29, pages 29–35 (2021)
Abstract. This paper proposes a novel framework based on arecently introduced classifier called multi-local power mean fuzzyk-nearest neighbor (MLPM-FKNN) and the Minkowski distanceto classify biomass feedstocks into property-based classes. Theproposed approach uses k nearest neighbors from each class tocompute class-representative multi-local power mean vectors andthe Minkowski distance instead of the Euclidean distance to fitthe most suitable distance metric based on the properties of thedata in finding the nearest neighbors to the new data point.We evaluate the performance of the proposed approach usingthree biomass datasets collected from several articles publishedin reputable journals and the Phyllis 2 biomass database. Inputfeatures of the biomass samples include their characteristics fromthe proximate analysis and ultimate analysis. In the developedframework, we interpret the biomass feedstocks classification asa five-class problem, and the classification performance of theproposed approach is benchmarked with the results obtainedfrom classical k-nearest neighbor-, fuzzy k-nearest neighbor- andsupport vector machine classifiers. Experimental results showthat the proposed approach outperforms the benchmarks andverify its effectiveness as a suitable tool for biomass classificationproblems. It is also evident from the results that the featuresfrom both ultimate and proximate analyses can offer a betterclassification of biomass feedstocks than the features consideredfrom each of those analyses separately.
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