TY - GEN
T1 - An efficient attribute reduction algorithm
AU - He, Yuguo
PY - 2006
Y1 - 2006
N2 - Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.
AB - Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.
UR - https://www.scopus.com/pages/publications/33750545769
U2 - 10.1007/11875581_103
DO - 10.1007/11875581_103
M3 - Conference contribution
AN - SCOPUS:33750545769
SN - 3540454853
SN - 9783540454854
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 859
EP - 868
BT - Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings
PB - Springer Verlag
T2 - 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
Y2 - 20 September 2006 through 23 September 2006
ER -