Aim of this paper is to present a method to improve the classification performance of a Fuzzy C-means based classifier. The obtained results show that this method can improve the performance of the classifier both in terms of computational efficiency (by reducing the amount of data to be analyzed) and in terms of classification error rate. The proposed method is based on the Semi-Pivoted QR approximation (SPQR) algorithm. It reduces a numeric dataset (a matrix) to its more important features (where each feature is a column of the matrix). The framework discussed in this article can be used by researchers and practi-tioners to set up high-performance machine learning systems
Improving Classification Performance Using the Semi-pivoted QR Approximation Algorithm / Popolizio, M.; Amato, A.; Piuri, V.; Di Lecce, V.. - 434:(2022), pp. 263-271. [10.1007/978-981-19-1122-4_29]
Improving Classification Performance Using the Semi-pivoted QR Approximation Algorithm
Popolizio M.
;Di Lecce V.
2022-01-01
Abstract
Aim of this paper is to present a method to improve the classification performance of a Fuzzy C-means based classifier. The obtained results show that this method can improve the performance of the classifier both in terms of computational efficiency (by reducing the amount of data to be analyzed) and in terms of classification error rate. The proposed method is based on the Semi-Pivoted QR approximation (SPQR) algorithm. It reduces a numeric dataset (a matrix) to its more important features (where each feature is a column of the matrix). The framework discussed in this article can be used by researchers and practi-tioners to set up high-performance machine learning systemsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
