000			02006naa a2200229 a 4500
003			AR-LpUFIB
005			20250311170448.0
008			230201s2015 xx o 000 0 eng d
024	8		_aDIF-M7609 _b7829 _zDIF006954
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aLanzarini, Laura Cristina
245	1	0	_aObtaining classification rules using lvqPSO
300			_a1 archivo (161,7 kB)
500			_aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520			_aTechnological advances nowadays have made it possible for processes to handle large volumes of historic information whose manual processing would be a complex task. Data mining, one of the most significant stages in the knowledge discovery and data mining (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called lvqPSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with several methods proposed by other authors and measured over 15 databases, and satisfactory results were obtained.
534			_aAdvances in Swarm and Computational Intelligence: 6th International Conference, ICSI 2015, held in conjunction with the Second BRICS Congress, CCI 2015, Beijing, China, June 25-28, 2015, Proceedings, Part I, pp. 183-193.
700	1		_aVilla Monte, Augusto
700	1		_aAquino, Germán
700	1		_aDe Giusti, Armando Eduardo
856	4	0	_uhttps://doi.org/10.1007/978-3-319-20466-6_20
942			_cCP
999			_c56730 _d56730