Feature selection with RVM and its application to prediction modeling

被引：0

作者：

Li, Dingfang ^{[1
]}

Hu, Wenchao ^{[1
]}

机构：

[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China

来源：

AI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2006年 / 4304卷

关键词：

RVM; feature selection; solubility; permeability; prediction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe here a method named FSRVM-PLS for model construction using relevance vector machine (RVM). The most compelling feature of FSRVM-PLS is that it's not necessary to estimate parameters in the feature selection phase benefiting from a fully probabilistic framework. After evaluating the effectiveness of FSRVM on a synthetic data set, our method is applied successfully to the prediction of aqueous solubility and permeability.

引用

页码：1140 / +

页数：2

共 50 条

[1] An integrated scheme for feature selection and parameter setting in the support vector machine modeling and its application to the prediction of pharmacokinetic properties of drugs
Yang, Sheng-Yong
Huang, Qi
Li, Lin-Li
Ma, Chang-Ying
Zhang, Hui
Bai, Ru
Teng, Qi-Zhi
Xiang, Ming-Li
Wei, Yu-Quan
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2009, 46 (02) : 155 - 163
[2] Feature selection and prediction modeling of drug responsiveness in Pharmacogenomics
Kim, Kyuhwan
Kim, Wonkuk
KOREAN JOURNAL OF APPLIED STATISTICS, 2021, 34 (02) : 153 - 166
[3] The Study on Feature Selection in Customer Churn Prediction Modeling
Wu, Yin
Qi, Jiayin
Wang, Chen
2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9, 2009, : 3205 - +
[4] A causal feature selection algorithm for stock prediction modeling
Zhang, Xiangzhou
Hu, Yong
Xie, Kang
Wang, Shouyang
Ngai, E. W. T.
Liu, Mei
NEUROCOMPUTING, 2014, 142 : 48 - 59
[5] Feature Selection based on Random Forests and its Application to a Modeling of Sewage Treatment Plant
Kimura, Shuhei
Tokuhisa, Masato
Uemura, Akiyuki
Okada, Mariko
2019 INTERNATIONAL CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI), 2019,
[6] System Entropy and Its Application in Feature Selection
ZHAO Jun~(1
2.College of Computer Science and Engineering
TheJournalofChinaUniversitiesofPostsandTelecommunications, 2004, (01) : 100 - 105
[7] Improved WOA and its application in feature selection
Liu, Wei
Guo, Zhiqing
Jiang, Feng
Liu, Guangwei
Wang, Dong
Ni, Zishun
PLOS ONE, 2022, 17 (05):
[8] A Novel Feature Selection Approach and Its Application
Zhang, Gexiang
Jin, Weidong
Hu, Laizhao
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, 3314 : 665 - 671
[9] A novel feature selection method and its application
Li, Bing
Chow, Tommy W. S.
Huang, Di
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2013, 41 (02) : 235 - 268
[10] A novel feature selection method and its application
Bing Li
Tommy W. S. Chow
Di Huang
Journal of Intelligent Information Systems, 2013, 41 : 235 - 268

← 1 2 3 4 5 →