TY - GEN
T1 - Using rough sets and maximum similarity graphs for nearest prototype classification
AU - Villuendas-Rey, Yenny
AU - Caballero-Mota, Yailé
AU - García-Lorenzo, María Matilde
PY - 2012
Y1 - 2012
N2 - The nearest neighbor rule (NN) is one of the most powerful yet simple non parametric classification techniques. However, it is time consuming and it is very sensitive to noisy as well as outlier objects. To solve these deficiencies several prototype selection methods have been proposed by the scientific community. In this paper, we propose a new editing and condensing method. Our method combines the Rough Set theory and the Compact Sets structuralizations to obtain a reduced prototype set. Numerical experiments over repository databases show the high quality performance of our method according to classifier accuracy.
AB - The nearest neighbor rule (NN) is one of the most powerful yet simple non parametric classification techniques. However, it is time consuming and it is very sensitive to noisy as well as outlier objects. To solve these deficiencies several prototype selection methods have been proposed by the scientific community. In this paper, we propose a new editing and condensing method. Our method combines the Rough Set theory and the Compact Sets structuralizations to obtain a reduced prototype set. Numerical experiments over repository databases show the high quality performance of our method according to classifier accuracy.
KW - editing methods
KW - nearest neighbor
KW - prototype selection
UR - http://www.scopus.com/inward/record.url?scp=84865600676&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33275-3_37
DO - 10.1007/978-3-642-33275-3_37
M3 - Contribución a la conferencia
SN - 9783642332746
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 300
EP - 307
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 17th Iberoamerican Congress, CIARP 2012, Proceedings
T2 - 17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012
Y2 - 3 September 2012 through 6 September 2012
ER -