ReliefF for Multi-label Feature Selection

• Published in: 2013 Brazilian Conference on Intelligent Systems pp. 6 - 11
• Main Authors: Spolaor, Newton, Alvares Cherman, Everton, Monard, Maria Carolina, Lee, Huei Diana
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2013
• Subjects: Educational institutions; feature ranking; filter feature selection; Hamming distance; High definition video; Noise; Noise level; RReliefF; systematic review; Systematics; Transforms
• DOI: 10.1109/BRACIS.2013.10

The feature selection process aims to select a subset of relevant features to be used in model construction, reducing data dimensionality by removing irrelevant and redundant features. Although effective feature selection methods to support single-label learning are abound, this is not the case for multi-label learning. Furthermore, most of the multi-label feature selection methods proposed initially transform the multi-label data to single-label in which a traditional feature selection method is then applied. However, the application of single-label feature selection methods after transforming the data can hinder exploring label dependence, an important issue in multi-label learning. This work proposes a new multi-label feature selection algorithm, RF-ML, by extending the single-label feature selection ReliefF algorithm. RF-ML, unlike strictly univariate measures for feature ranking, takes into account the effect of interacting attributes to directly deal with multi-label data without any data transformation. Using synthetic datasets, the proposed algorithm is experimentally compared to the ReliefF algorithm in which the multi-label data has been previously transformed to single-label data using two well-known data transformation approaches. Results show that the proposed algorithm stands out by ranking the relevant features as the best ones more often.