Results 231 to 240 of about 87,928 (288)

HFSA: hybrid feature selection approach to improve medical diagnostic system. [PDF]

PeerJ Comput Sci
Rabie AH, Aldawsari M, Saleh AI, Saraya MS, Rashad M.   +4 more
europepmc   +1 more source

Enhanced fNIRS-Based MCI Detection via Resting-State and Task-State Integration With Spatial-Temporal Feature Reduction. [PDF]

IEEE J Transl Eng Health Med
Zhang C, Yang H, Wang J, Xiang K, Chen J, Peng L, Fan C, Wu Y, Hou ZG.   +8 more
europepmc   +1 more source

RNAdvisor 2: A unified platform for RNA 3D model quality assessment using metrics, scoring functions, and meta-metrics. [PDF]

Sci Rep
Bernard C, Postic G, Ghannay S, Tahi F.
europepmc   +1 more source

HFSOF: A Hierarchical Feature Selection and Optimization Framework for Ultrasound-Based Diagnosis of Endometrial Lesions. [PDF]

Biomimetics (Basel)
Liu Y, Zhang Z, Chai T, Zhao H.
europepmc   +1 more source

Hybrid Synthetic Minority Over-sampling Technique (HSMOTE) and Ensemble Deep Dynamic Classifier Model (EDDCM) for big data analytics. [PDF]

Sci Rep
M P, Tyagi B, R NP, B M.
europepmc   +1 more source

ACG-SFE: Adaptive cluster-guided simple, fast, and efficient feature selection for high-dimensional microarray data in binary classification. [PDF]

PLoS One
Tye YW, Chew X, Yusof UK, Tulpar S.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Filter-Wrapper Hybrid Method on Feature Selection

2010 Second WRI Global Congress on Intelligent Systems, 2010
Feature selection is a process commonly used in machine learning. This paper examines two broad classes of feature selection methods: filter methods and wrapper methods to find their individual advantages and disadvantages. This paper selects their different merits to propose a filter-Wrapper hybrid method (FWHM) to optimize the efficiency of feature ...
Hu Min, Wu Fangfang
exaly   +2 more sources

Combining functional networks and sensitivity analysis as wrapper method for feature selection

Expert Systems With Applications, 2011
In this paper, a new wrapper method for feature selection, namely IAFN-FS (Incremental ANalysis Of VAriance and Functional Networks for Feature Selection) is presented. The method uses as induction algorithm the AFN (ANOVA and Functional Networks) learning method; follows a backward non-sequential strategy from the complete set of features (thus ...
Noelia Sánchez-Maroño, Amparo Alonso Betanzos   +1 more
exaly   +2 more sources