scholarly journals Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection

2017 ◽  
Vol 4 (2) ◽  
pp. 024507 ◽  
Author(s):  
Kadayanallur Mahadevan Prabusankarlal ◽  
Palanisamy Thirumoorthy ◽  
Radhakrishnan Manavalan
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Rough Set ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Adaptive Differential Evolution ◽  
Learning Machine ◽  
Self Adaptive

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine

scholarly journals Feature Selection Optimization using Hybrid Relief-f with Self-adaptive Differential Evolution

2017 ◽  
Vol 10 (3) ◽  
pp. 21-29 ◽  
Author(s):  
M Zainudin ◽  
◽  
Md Sulaiman ◽  
Norwati Mustapha ◽  
Thinagaran Perumal ◽  
...  
Keyword(s):  
Feature Selection ◽  
Differential Evolution ◽  
Adaptive Differential Evolution ◽  
Self Adaptive

Predicting Human Actions Using a Hybrid of ReliefF Feature Selection and Kernel-Based Extreme Learning Machine

2018 ◽  
pp. 379-397 ◽  
Author(s):  
Musa Peker ◽  
Serkan Ballı ◽  
Ensar Arif Sağbaş
Keyword(s):  
Feature Selection ◽  
Extreme Learning Machine ◽  
Human Action ◽  
High Accuracy ◽  
Attribute Selection ◽  
Sensor Data ◽  
Motion Sensor ◽  
Sensor Signals ◽  
Learning Machine

Human activity recognition (HAR) is a growing field that provides valuable information about a person. Sensor-equipped smartwatches stand out in these studies in terms of their portability and cost. HAR systems usually preprocess raw signals, decompose signals, and then extract attributes to be used in the classifier. Attribute selection is an important step to reduce data size and provide appropriate parameters. In this chapter, classification of eight different actions (brushing teeth, walking, running, vacuuming, writing on the board, writing on paper, using the keyboard, and stationary) has been performed with smartwatch motion sensor data. Forty-two different features have been extracted from the motion sensor signals and the feature selection has been performed with the ReliefF algorithm. After that, performance evaluation has been performed with four different machine learning methods. With this study in which the best results have been obtained with the kernel-based extreme learning machine (KELM) algorithm, estimation of human action has been performed with high accuracy.

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