Hierarchical classification of microorganisms based on high-dimensional phenotypic data

2017 ◽  
Vol 11 (3) ◽  
pp. e201700047 ◽  
Author(s):  
Valeria Tafintseva ◽  
Evelyne Vigneau ◽  
Volha Shapaval ◽  
Véronique Cariou ◽  
El Mostafa Qannari ◽  
...  
Keyword(s):  
Hierarchical Classification ◽  
High Dimensional ◽  
Phenotypic Data

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

scholarly journals A Medical Cloud-Based Platform for Respiration Rate Measurement and Hierarchical Classification of Breath Disorders

Sensors ◽  
2014 ◽  
Vol 14 (6) ◽  
pp. 11204-11224 ◽  
Author(s):  
Atena Fekr ◽  
Majid Janidarmian ◽  
Katarzyna Radecka ◽  
Zeljko Zilic
Keyword(s):  
Respiration Rate ◽  
Hierarchical Classification ◽  
Rate Measurement ◽  
Medical Cloud ◽  
Respiration Rate Measurement

scholarly journals GPCRTree: online hierarchical classification of GPCR function

2008 ◽  
Vol 1 (1) ◽  
pp. 67 ◽  
Author(s):  
Matthew N Davies ◽  
Andrew Secker ◽  
Mark Halling-Brown ◽  
David S Moss ◽  
Alex A Freitas ◽  
...  
Keyword(s):  
Hierarchical Classification

scholarly journals Automated hierarchical classification of protein domain subfamilies based on functionally-divergent residue signatures

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew F Neuwald ◽  
Christopher J Lanczycki ◽  
Aron Marchler-Bauer
Keyword(s):  
Hierarchical Classification ◽  
Protein Domain
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