Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection
IEEE Access, 2020 Recent years, multi-label feature selection has gradually attracted significant attentions from machine learning, statistical computing and related communities and has been widely applied to diverse problems from music recognition to text mining, image ...
Wanfu Gao +3 more
doaj +1 more source
Feature and Variable Selection in Classification [PDF]
, 2014 The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not lend themselves
Karper, Aaron
core
Scalable and Accurate Online Feature Selection for Big Data
, 2016 Feature selection is important in many big data applications. Two critical challenges closely associate with big data. Firstly, in many big data applications, the dimensionality is extremely high, in millions, and keeps growing.
Ding, Wei +3 more
core +1 more source
Mitochondrial fatty acid oxidation is stimulated by red light irradiation
FEBS Letters, EarlyView.Light at different wavelengths has distinct effects on keratinocyte viability and metabolism. UVA light abrogates metabolic fluxes. Blue and green light have no effect on metabolic fluxes, while red light enhanced oxidative phosphorylation by promoting fatty acid oxidation. Keratinocytes are the primary constituents of sunlight‐exposed epidermis.
Manuel Alejandro Herrera +4 more
wiley +1 more source
Feature Selection in k-Median Clustering [PDF]
, 2004 An e ective method for selecting features in clustering unlabeled data is proposed based on changing the objective function of the standard k-median clustering algorithm.
Mangasarian, Olvi, Wild, Edward
core +1 more source
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
, 2015 Feature selection is essential for effective visual recognition. We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost
Baluja, Shumeet +3 more
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Vacuolar transport and function of Saccharomyces cerevisiae sterol ester hydrolase Tgl1
FEBS Letters, EarlyView.Tgl1, one of yeast sterol ester hydrolases, had been found on the lipid droplets where sterol esters are mainly stored. This study revealed that Tgl1 is transported into the vacuole depending on the ESCRT‐I–III complex, and that it exhibits intra‐vacuolar sterol ester hydrolase activity.
Takumi Nakatsuji +5 more
wiley +1 more source
Staging of Prostate Cancer Using Automatic Feature Selection, Sampling and Dempster-Shafer Fusion
Cancer Informatics, 2009 A novel technique of automatically selecting the best pairs of features and sampling techniques to predict the stage of prostate cancer is proposed in this study.
Sandeep Chandana +2 more
doaj
An incremental approach to MSE-based feature selection [PDF]
, 2007 Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training.
Bao, C, Guan, SU, Qi, Y
core
Parkinson’s Disease Detection Using Filter Feature Selection and a Genetic Algorithm with Ensemble Learning [PDF]
, 2023 Abdullah Ali +2 more
openalex +1 more source