Multi-label Feature Selection via Global Relevance and Redundancy Optimization

Abstract

<jats:p>Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.</jats:p>

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

“…Feature selection method can reduce spatial dimensions and decrease model training time. Therefore, this article uses principal component analysis (PCA) ( Abdi and Williams, 2010 ), GRRO ( Zhang et al , 2020a,b,c ), MDFS ( Zhang et al , 2019 ), MDDM ( Zhang and Zhou, 2010 ), MVMD ( Xu et al , 2016 ), MLSI ( Yu et al , 2005 ) to eliminate irrelevant features. Through LOOCV test, the feature subset obtained by each method is put into MLFE.…”

“…Five datasets are used to verify the effectiveness of the model. The Gram-positive bacteria dataset ( Dehzangi et al , 2015 ) is the training set, while the Gram-negative bacteria dataset ( Dehzangi et al , 2015 ), the virus dataset ( Shen and Chou, 2010 ), the SARS-CoV-2 dataset ( Zhang et al , 2020 b ) and the newPlant dataset ( Wan et al , 2012 ) are the test sets together. The Gram-positive bacteria dataset, Gram-negative bacteria dataset, virus dataset come from the Swiss-Prot database and the breakdown of each dataset is shown in Supplementary Tables S1–S3 .…”

“…When protein structure changes, it will cause diseases, such as kidney disease ( Ivanova et al , 2008 ), myocarditis ( Jang et al , 1988 ), diabetes ( Brownlee, 1995 ), dermatomyositis ( Brownlee, 1995 ) and muscle atrophy ( Sneddon et al , 2000 ). With the continuous increase of data and the expansion of research directions ( Wan et al , 2015 , 2017 ), the traditional machine learning methods cannot achieve good prediction results ( Marilyn et al , 2020 ; Zhang et al , 2020 c ). First, the traditional machine learning methods are time-consuming and labor-intensive.…”

“…Since protein sequences cannot be directly used for calculation, it must be transformed into digital information for further study ( Yu et al , 2020a ). Zhang et al (2020 c ) utilized dipeptide composition (DC), pseudo position-specific scoring matrix (PsePSSM), pseudo amino acid composition (PseAAC), gene ontology (GO) and encoding based on grouped weight (EBGW) to extract protein information from relevant datasets. Wu et al (2012) adopted GO and evolutionary information to develop a new predictor iLoc-Gpos in Gram-positive bacteria dataset.…”

“… Xu et al (2016) came up with the ML feature extraction algorithm via feature variance and feature-label dependence (MVMD) method, which integrated two least squares formulas and used the maximum feature variance and the correlation of feature label to select the best feature vector. Zhang et al (2020a ) presented global relevance and redundancy optimization (GRRO) method composed of feature relevance, label relevance and feature redundancy, which greatly improved computing efficiency.…”