A tree-based algorithm for attribute selection

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 4; pp. 821 - 833
• Main Authors: Baranauskas, José Augusto, Netto, Oscar Picchi, Nozawa, Sérgio Ricardo, Macedo, Alessandra Alaniz
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2018; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Compression tests; Computer Science; Data compression; Data mining; Datasets; Feature selection; Machine learning; Machines; Manufacturing; Mechanical Engineering; Performance degradation; Processes; Time compression; Attribute selection; High dimensional data; Data pre-processing; Filter; Decision tree
• ISSN: 0924-669X; 1573-7497; 1573-7497
• DOI: 10.1007/s10489-017-1008-y

This paper presents an improved version of a decision tree-based filter algorithm for attribute selection. This algorithm can be seen as a pre-processing step of induction algorithms of machine learning and data mining tasks. The filter was evaluated based on thirty medical datasets considering its execution time, data compression ability and AUC (Area Under ROC Curve) performance. On average, our filter was faster than Relief-F but slower than both CFS and Gain Ratio. However for low-density (high-dimensional) datasets, our approach selected less than 2% of all attributes at the same time that it did not produce performance degradation during its further evaluation based on five different machine learning algorithms.