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Search: "[ keyword: Random Forest ]" (8)
Research on the modified algorithm for improving accuracy of Random Forest classifier which identifies automatically arrhythmia
Hyun Ju Lee , Dong Kyoo Shin , Hee Won Park , Soo Han Kim , Dong Il Shin The KIPS Transactions:PartB ,
Vol. 18, No. 6, pp. 341-348,
Dec.
2011
10.3745/KIPSTB.2011.18.6.341
10.3745/KIPSTB.2011.18.6.341
A K-Means-Based Clustering Algorithm for Traffic Prediction in a Bike-Sharing System
Kyoungok Kim, Chang Hwan Lee KIPS Transactions on Software and Data Engineering,
Vol. 10, No. 5, pp. 169-178,
May.
2021
https://doi.org/10.3745/KTSDE.2021.10.5.169
Keywords: Bike Sharing System, clustering, demand prediction, Random Forest
https://doi.org/10.3745/KTSDE.2021.10.5.169
Keywords: Bike Sharing System, clustering, demand prediction, Random Forest
Mortality Prediction of Older Adults Using Random Forest and Deep Learning
Junhyeok Park, Songwook Lee KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 10, pp. 309-316,
Oct.
2020
https://doi.org/10.3745/KTSDE.2020.9.10.309
Keywords: Mortality Prediction, Convolutional Neural Network, Random Forest, Feature selection, Deep Learning
https://doi.org/10.3745/KTSDE.2020.9.10.309
Keywords: Mortality Prediction, Convolutional Neural Network, Random Forest, Feature selection, Deep Learning
An Energy Consumption Prediction Model for Smart Factory Using Data Mining Algorithms
Sathishkumar V E, Myeongbae Lee, Jonghyun Lim, Yubin Kim, Changsun Shin, Jangwoo Park, Yongyun Cho KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 5, pp. 153-160,
May.
2020
https://doi.org/10.3745/KTSDE.2020.9.5.153
Keywords: energy consumption, Data Mining, Random Forest, linear regression, Gradient Boosting Machine, Support Vector Machine
https://doi.org/10.3745/KTSDE.2020.9.5.153
Keywords: energy consumption, Data Mining, Random Forest, linear regression, Gradient Boosting Machine, Support Vector Machine
Hourly Prediction of Particulate Matter (PM2.5) Concentration Using Time Series Data and Random Forest
Deukwoo Lee, Soowon Lee KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 4, pp. 129-136,
Apr.
2020
https://doi.org/10.3745/KTSDE.2020.9.4.129
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
https://doi.org/10.3745/KTSDE.2020.9.4.129
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
Coreference Resolution for Korean Using Random Forests
Seok-Won Jeong, MaengSik Choi, HarkSoo Kim KIPS Transactions on Software and Data Engineering,
Vol. 5, No. 11, pp. 535-540,
Nov.
2016
10.3745/KTSDE.2016.5.11.535
Keywords: Coreference Resolution, Random Forest, Sieve-Guided Features
10.3745/KTSDE.2016.5.11.535
Keywords: Coreference Resolution, Random Forest, Sieve-Guided Features
Diagnosis of Parkinson"s Disease Using Two Types of Biomarkers and Characterization of Fiber Pathways
Shin Ttae Kang , Wook Lee , Byung Kyu Park , Kyung Sook Han KIPS Transactions on Software and Data Engineering,
Vol. 3, No. 10, pp. 421-428,
Oct.
2014
10.3745/KTSDE.2014.3.10.421
10.3745/KTSDE.2014.3.10.421
Medical Image Classification and Retrieval Using BOF Feature Histogram with Random Forest Classifier
Son Jung Eun , Ko Byoung Chul , Nam Jae Yeal KIPS Transactions on Software and Data Engineering,
Vol. 2, No. 4, pp. 273-280,
Apr.
2013
10.3745/KTSDE.2013.2.4.273
10.3745/KTSDE.2013.2.4.273