## Using the generalized fuzzy k-nearest neighbor classifier for biomass feedstocks classification

### Mahinda Mailagaha Kumbure, Pasi Luukka

DOI: http://dx.doi.org/10.15439/2021B5

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.

### References

- A. Demirbas, “Biomass feedstocks,” In: Biofuels; Green Energy and Technology, Springer, 2009, pp. 45-85.
- S. Gent and M. Twedt and C. Gerometta and E. Almberg, "Chapter Two - Introduction to Feedstocks," in Theoretical and Applied Aspects of Biomass Torrefaction, Butterworth-Heinemann, 2017, pp. 17-39
- A. A. Adeleke and J. K. Odusote and P. P. Ikubanni and O. A. Lasode, and M. Malathi, and D. Paswan,“The ignitability, fuel ratio and ash fusion temperatures of torrefied woody biomass,” Heliyon, vol. 6, 2020, pp. e03582.
- A.A. Adeleke and P.P. Ikubanni and T.A. Orhadahwe and C.T. Christopher and J.M. Akano and O.O. Agboola and S.O. Adegoke and A.O. Balogun and R.A. Ibikunle, “Sustainability of multifaceted usage of biomass: A review,” Heliyon, vol. 7, 2021, pp. e08025.
- O. O. Olatunji and S. Akinlabi and N. Madushele, “Property-based biomass feedstock grading using k-nearest neighbor technique,” Energy, vol. 190, 2020, pp. 116346.
- P. Basu, Chapter 2 - Biomass Characteristics. Biomass Gasification and Pyrolysis, 2010, pp. 27-63.
- A. A. Khan and W. D. Jong and P. J. Jansens and H. Spliethoff, “Biomass combustion in fluidized bed boilers: Potential problems and remedies,” Fuel Process, vol. 90, 2009, pp. 21-50.
- A. Nag and A. Gerritsen and C. Doeppke and A. E. Harman-Ware, “Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data,” Int. J. Mol. Sci., vol. 22, 2021, pp. 4107.
- G. Tao and T. A. Lestander and P. Geladi and S. Xiong, “Biomass properties in association with plant species and assortments I: a synthesis based on literature data of energy properties,” Renew. Sustain. Energy Rev., vol. 16, 2012, pp. 3481-3506.
- M. Wang et al., “To distinguish the primary characteristics of agro-waste biomass by the principal component analysis: An investigation in East China,” Waste Manage., vol. 90, 2019, pp. 100-120.
- T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. 13, 1967, pp. 21-27.
- M. M. Kumbure and P. Luukka and M. Collan, “An enhancement of fuzzy k-nearest neighbor classifier using multi-local power means,” Proc. 11th Conf. European Society for Fuzzy Logic and Technology (EUSFLAT), Atlantis Press. 2019, pp. 83-90.
- J. Parikh and S. A. Channiwala and G. K. Ghosal, “A correlation for calculating HHV from proximate analysis of solid fuels,” Fuel, vol. 84, 2005, pp. 487-494.
- D. R. Nhuchhen and P. A. Salam, “Estimation of higher heating value of biomass from proximate analysis: A new approach,” Fuel, vol. 99, 2012, pp. 55-63.
- S. V. Vassilev and D. Baxter and L. K. Andersen and C. G. Vassileva, “An overview of the chemical composition of biomass,” Fuel vol. 89, 2010, pp. 913-933.
- M. Sajdak and O. Piotrowski, “C&RT model application in classification of biomass for energy production and environmental protection,” Cent. Eur. J. Chem., vol. 11, 2013, pp. 259-270
- Energy Research Centre of the Netherlands. Phyllis 2: database for biomass and waste, [Online]. Available: https://phyllis.nl/Browse/Standard/ECN-Phyllis#eucalyptus. [Accessed: July 31, 2021].
- J. M. Keller and M. R. Gray and J. A. Givens, “A Fuzzy K-Nearest Neighbor Algorithm,” EEE Trans. Syst. Man Cybern. Syst. , vol. 15, 1985, pp. 580-585.
- C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, 1995, pp. 273-297.
- B. Salami and K. Haataja and P. Toivanen, “State-of-the-Art Techniques in Artificial Intelligence for Continual Learning: A Review” Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, and L. Maciaszek, M. Paprzycki and D. Śl ̨ezak, Eds. ACSIS, vol. 26, 2021, pp. 23-32.
- P. Gepner, “Machine Learning and High-Performance Computing Hybrid Systems, a New Way of Performance Acceleration in Engineering and Scientific Applications” Proceedings of the 16th Conference on Computer Science and Intelligence Systems, M. Ganzha, and L. Maciaszek, M. Paprzycki and D. Ślęzak, Eds. ACSIS, vol. 212, 2021, pp. 27-36.
- A. Coluccia and A. Fascista and G. Ricci, “A k-nearest neighbors approach to the design of radar detectors,” Signal Process., vol. 174, 2020, pp. 107609.
- R. Arian and A. Hariri and A. Mehridehnavi and A. Fassihi and F. Ghasemi, “Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm,” Comput. Biol. Chem., vol. 86, 2020, pp. 107269.
- S. Wua et al., “Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis,” Comput. Biol. Med., vol. 135, 2021, pp. 104582.
- M. M. Kumbure and P. Luukka and M. Collan, “A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean,” Pattern Recognit. Lett., vol. 140, 2020, pp. 172-178.
- S. Arlot and A. Celisse, “A survey of cross-validation procedures for model selection,” Stat Surv., vol. 4, 2010, pp. 40–79.
- P. Vuttipittayamongkol and E. Elyan and A. Petrovski, “On the class overlap problem in imbalanced data classification,” Knowl.-Based Syst., vol. 212, 2021, pp. 106631