Hybrid Classification of High-Dimensional Biomedical Tumour Datasets

2015 ◽  
pp. 287-298 ◽  
Author(s):  
Liliana Byczkowska-Lipinska ◽  
Agnieszka Wosiak
Keyword(s):  
High Dimensional ◽  
Hybrid Classification

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 IDENTIFICATION AND CLASSIFICATION OF FOREST TYPES USING DATA LANDSAT 8 IN KARO, DAIRI, AND SAMOSIR DISTRICTS, NORTH SUMATRA

2017 ◽  
Vol 13 (2) ◽  
pp. 139
Author(s):  
Heru Noviar ◽  
Tatik Kartika
Keyword(s):  
Rehabilitation Program ◽  
Landsat 8 ◽  
Secondary Forests ◽  
Forest Types ◽  
Forest Classification ◽  
Hybrid Classification ◽  
Land Covers ◽  
Using Data ◽  
Identification Techniques

Forests have important roles in terms of carbon storage and other values. Various studies have been conducted to identify and distinguish the forest from non-forest classes. Several forest types classes such as secondary forests and plantations should be distinguished related to the restoration and rehabilitation program for dealing with climate change. The study was carried out to distinguish several classes of important forests such as the primary dryland forests, secondary dryland forest, and plantation forests using Landsat 8 to develop identification techniques of specific forests classes. The study areas selected were forest areas in three districts, namely Karo, Dairi, and Samosir of North Sumatera Province. The results showed that using composite RGB 654 of Landsat 8 imagery based on test results OIF for the forest classification, the forests could be distinguished with other land covers. Digital classification can be combined with the visual classification known as a hybrid classification method, especially if there are difficulties in border demarcation between the two types of forest classes or two classes of land covers.

scholarly journals Methodology Proposal of ADHD Classification of Children Based on Cross Recurrence Plots

2021 ◽  
Author(s):  
Marco Aceves-Fernandez
Keyword(s):  
Machine Learning ◽  
Spectral Distribution ◽  
High Dimensional ◽  
Control Group ◽  
Machine Learning Method ◽  
Learning Method ◽  
Recurrence Plots ◽  
Eeg Signals ◽  
Power Spectral

Abstract Dealing with electroencephalogram signals (EEG) are often not easy. The lack of predicability and complexity of such non-stationary, noisy and high dimensional signals is challenging. Cross Recurrence Plots (CRP) have been used extensively to deal with detecting subtle changes in signals even when the noise is embedded in the signal. In this contribution, a total of 121 children performed visual attention experiments and a proposed methodology using CRP and a Welch Power Spectral Distribution have been used to classify then between those who have ADHD and the control group. Additional tools were presented to determine to which extent the proposed methodology is able to classify accurately and avoid misclassifications, thus demonstrating that this methodology is feasible to classify EEG signals from subjects with ADHD. Lastly, the results were compared with a baseline machine learning method to prove experimentally that this methodology is consistent and the results repeatable.

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