k-Segments Classifier - A Non-Linear Approach for the Classification of Sampling Data

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Abstract: This paper proposes a method to classify sampling data based on the k-segments algorithm. The data classification efficiency is relevant for this multivariate statistical analysis technique. The method consists of adjusting an a priori defined polygonal line for each class. A new observation is then classified into the class for which polygonal line it has the smallest orthogonal distance. Experimentally, the algorithm is applied to several sets of sampling data and the results are compared with the apparent error rate, which demonstrates the good performance of this methodology.

WSEAS Transactions on Mathematics, ISSN / E-ISSN: 1109-2769 / 2224-2880, Volume 14, 2015, Art. #29

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Zaudir Dal Cortivo, Jair Mendes Marques, "k-Segments Classifier - A Non-Linear Approach for the Classification of Sampling Data," WSEAS Transactions on Mathematics, vol. 14, pp. 301-312, 2015, DOI:
Zaudir Dal Cortivo, Jair Mendes Marques. k-Segments Classifier - A Non-Linear Approach for the Classification of Sampling Data. WSEAS Transactions on Mathematics. 2015;14:301-312.
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