Genetic nearest feature plane |
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| Authors: | Loris Nanni Alessandra Lumini |
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| Affiliation: | 1. Climate Change Research Division, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Republic of Korea;2. Center for Convergent Chemical Process, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of Korea;3. University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea;1. CNR-ITAE, Istituto di Tecnologie Avanzate per l’Energia “Nicola Giordano”, Via S. Lucia 5, 98126 Messina, Italy;2. Dipartimento di Ingegneria per l’Ambiente e il Territorio e Ingegneria Chimica, Università della Calabria, Via P. Bucci, Rende, 97036, Italy;1. Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, Lahore-54000, Pakistan;2. Refinery Division, Pak-Arab Refinery Limited “Company” (PARCO), Corporate Headquarters, Korangi Creek Road, Karachi-75190, Pakistan;3. Department of Chemistry, COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, Lahore-54000, Pakistan;4. Department of Chemistry, The University of Lahore, 1-km Defence Road, Off Raiwind Road, Lahore-54000, Pakistan;5. School of Environmental Engineering, University of Seoul, Dongdaemun-Gu 02504, Republic of Korea;6. Department of Chemical Engineering, Kongju National University, Cheonan 31081, Republic of Korea |
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| Abstract: | The problem addressed in this paper concerns the complexity reduction of the nearest feature plane classifier, so that it may be applied also in dataset where the training set contains many patterns. This classifier considers, to classify a test pattern, the subspaces created by each combination of three training patterns. The main problem is that in dataset of high cardinality this method is unfeasible.A genetic algorithm is here used for dividing the training patterns in several clusters which centroids are used to build the feature planes used to classify the test set.The performance improvement with respect to other nearest neighbor based classifiers is validated through experiments with several benchmark datasets. |
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