Coalbody structure classification method based on dual-lateral and RXO crossplot analysis

2013 ◽  
Vol 19 (4) ◽  
pp. 522-529 ◽  
Author(s):  
Qiang Chen ◽  
Hui-Fang Yao ◽  
Suo-Liang Chang ◽  
Jun Liu
Keyword(s):  
Classification Method

An Automatic Classification Method for Involuntary and Two Types of Voluntary Blinks

2016 ◽  
Vol 136 (9) ◽  
pp. 1350-1358 ◽  
Author(s):  
Hironobu Sato ◽  
Kiyohiko Abe ◽  
Shoichi Ohi ◽  
Minoru Ohyama
Keyword(s):  
Automatic Classification ◽  
Classification Method

Bone Density Classification Method for Robotic Drilling System

2013 ◽  
Vol 133 (3) ◽  
pp. 328-334 ◽  
Author(s):  
Koyo Yu ◽  
Yuki Saito ◽  
Yusuke Kasahara ◽  
Hiromasa Kawana ◽  
Shin Usuda ◽  
...  
Keyword(s):  
Bone Density ◽  
Classification Method ◽  
Robotic Drilling ◽  
Drilling System

Development of Fault Diagnosis Classification Method System using Mahalanobis Distance in Micro Wind Turbine

2015 ◽  
Vol 135 (9) ◽  
pp. 577-578
Author(s):  
Toshio Bouno ◽  
Shinichi Arimori ◽  
Toshifumi Yuji ◽  
Hiroyuki Kinoshita
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Mahalanobis Distance ◽  
Classification Method ◽  
Diagnosis Classification

Sử dụng phương pháp thang điểm có trọng số đánh giá tổng hợp tài nguyên khí hậu cho du lịch, nghỉ dưỡng (Tại một số trung tâm du lịch ở Việt Nam)

2008 ◽  
Vol 30 (4) ◽  
pp. 356-362
Author(s):  
Nguyễn Khanh Vân
Keyword(s):  
Classification Method ◽  
Viet Nam ◽  
Bioclimatic Conditions

Using the weighted classification method to evaluate bioclimatic conditions for tourism and concevalescence (in some tourism centers of Vietnam)

Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

Classification Method of Muddy Tidal Flat Wetlands Based on Remote Sensing

2013 ◽  
Vol 15 (4) ◽  
pp. 590 ◽  
Author(s):  
Cong WANG ◽  
Hongyu LIU ◽  
Minghang HOU ◽  
Qingmei TAN
Keyword(s):  
Remote Sensing ◽  
Tidal Flat ◽  
Classification Method
Sign in / Sign up

Export Citation Format

Share Document