scholarly journals Monte Carlo feature selection for supervised classification

2007 ◽  
Vol 24 (1) ◽  
pp. 110-117 ◽  
Author(s):  
M. Draminski ◽  
A. Rada-Iglesias ◽  
S. Enroth ◽  
C. Wadelius ◽  
J. Koronacki ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Supervised Classification ◽  
Selection For ◽  
Monte Carlo Feature Selection

scholarly journals A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rizwan Niaz ◽  
Ibrahim M. Almanjahie ◽  
Zulfiqar Ali ◽  
Muhammad Faisal ◽  
Ijaz Hussain
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Significant Role ◽  
Meteorological Data ◽  
Accurate Estimation ◽  
Relative Importance ◽  
Drought Prediction ◽  
Monte Carlo Feature Selection ◽  
Remaining Time ◽  
Scale Data

Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales.

Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment

2012 ◽  
Vol 119 (7) ◽  
pp. 821-831 ◽  
Author(s):  
Marcin Kruczyk ◽  
Henrik Zetterberg ◽  
Oskar Hansson ◽  
Sindre Rolstad ◽  
Lennart Minthon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Monte Carlo ◽  
Feature Selection ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Rule Based ◽  
Monte Carlo Feature Selection

Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

2014 ◽  
Vol 76 ◽  
pp. 352-359 ◽  
Author(s):  
Chanin Nantasenamat ◽  
Teerawat Monnor ◽  
Apilak Worachartcheewan ◽  
Prasit Mandi ◽  
Chartchalerm Isarankura-Na-Ayudhya ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Feature Selection ◽  
Aldose Reductase ◽  
Qsar Modeling ◽  
Aldose Reductase Inhibitors ◽  
Reductase Inhibitors ◽  
Monte Carlo Feature Selection
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