MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach t...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2007-03, Vol.18 (2), p.431-448 |
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| creator | Kian Hong Quah Quek, C. |
| description | Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. The results demonstrate that the MCES method has following strong advantages: 1) ability to identify correlated and irrelevant features based on weight ranking, 2) application to both nonlinear system estimation and classification tasks, and 3) independence of the underlying induction algorithms used to derive the performance measures |
| doi_str_mv | 10.1109/TNN.2006.887555 |
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(IEEE) 2007</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c374t-e27b5216c46e84021d874af711da6435cf0ba9c423c8df7d1fc5ff045029ee4a3</citedby><cites>FETCH-LOGICAL-c374t-e27b5216c46e84021d874af711da6435cf0ba9c423c8df7d1fc5ff045029ee4a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4118276$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27911,27912,54745</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4118276$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17385630$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kian Hong Quah</creatorcontrib><creatorcontrib>Quek, C.</creatorcontrib><title>MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNN</addtitle><addtitle>IEEE Trans Neural Netw</addtitle><description>Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. The results demonstrate that the MCES method has following strong advantages: 1) ability to identify correlated and irrelevant features based on weight ranking, 2) application to both nonlinear system estimation and classification tasks, and 3) independence of the underlying induction algorithms used to derive the performance measures</description><subject>Accuracy measure</subject><subject>adaptive neurofuzzy inference systems (ANFISs)</subject><subject>Adaptive systems</subject><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>audio signal classification</subject><subject>automobile miles per gallon (MPG) prediction</subject><subject>Automobiles</subject><subject>Classification</subject><subject>Computational efficiency</subject><subject>Computer Simulation</subject><subject>Data mining</subject><subject>Decision trees</subject><subject>Dynamical systems</subject><subject>Filters</subject><subject>financial data modeling</subject><subject>induction algorithm</subject><subject>Inference algorithms</subject><subject>Information Storage and Retrieval - methods</subject><subject>low computational cost</subject><subject>Models, Statistical</subject><subject>Monte Carlo evaluative feature selection</subject><subject>Monte Carlo evaluative selection (MCES)</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo methods</subject><subject>multilayer perceptron (MLP)</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Pattern classification</subject><subject>Pattern Recognition, Automated - methods</subject><subject>regressive series</subject><subject>reinforcement learning</subject><subject>Sampling methods</subject><subject>Studies</subject><subject>Tasks</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNp90b1rGzEYBnBRGprE6dyhUESHdDpH3x_djHHaQuIMSaGbkHWv6Jmz5Up3hvz3kbFpS4dOEtJPL3p4EHpHyZRSYm-elsspI0RNjdFSylfoglpBG0Isf133RMjGMqbP0WUpa0KokES9QedUcyMVJxfox_188fgZz_Ay7aHH92k7AJ773Ce82Pt-9EO3B_wIPYShS1s82-1y8uEnjinjh9X6cFzBLfhhzH_BcoXOou8LvD2tE_T9dvE0_9rcPXz5Np_dNYFrMTTA9EoyqoJQYARhtDVa-Kgpbb0SXIZIVt4GwXgwbdQtjUHGWHMRZgGE5xP06Ti3_uvXCGVwm64E6Hu_hTQWZwxRVtmadoKu_ys14cRKqyv8-A9cpzFvawpnKWNUMMYqujmikFMpGaLb5W7j87OjxB26cbUbd-jGHbupLz6cxo6rDbR__KmMCt4fQQcAv68FpYZpxV8A4eOQvA</recordid><startdate>20070301</startdate><enddate>20070301</enddate><creator>Kian Hong Quah</creator><creator>Quek, C.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20070301</creationdate><title>MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections</title><author>Kian Hong Quah ; Quek, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c374t-e27b5216c46e84021d874af711da6435cf0ba9c423c8df7d1fc5ff045029ee4a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Accuracy measure</topic><topic>adaptive neurofuzzy inference systems (ANFISs)</topic><topic>Adaptive systems</topic><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>audio signal classification</topic><topic>automobile miles per gallon (MPG) prediction</topic><topic>Automobiles</topic><topic>Classification</topic><topic>Computational efficiency</topic><topic>Computer Simulation</topic><topic>Data mining</topic><topic>Decision trees</topic><topic>Dynamical systems</topic><topic>Filters</topic><topic>financial data modeling</topic><topic>induction algorithm</topic><topic>Inference algorithms</topic><topic>Information Storage and Retrieval - methods</topic><topic>low computational cost</topic><topic>Models, Statistical</topic><topic>Monte Carlo evaluative feature selection</topic><topic>Monte Carlo evaluative selection (MCES)</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo methods</topic><topic>multilayer perceptron (MLP)</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Nonlinear dynamics</topic><topic>Nonlinear systems</topic><topic>Pattern classification</topic><topic>Pattern Recognition, Automated - methods</topic><topic>regressive series</topic><topic>reinforcement learning</topic><topic>Sampling methods</topic><topic>Studies</topic><topic>Tasks</topic><toplevel>online_resources</toplevel><creatorcontrib>Kian Hong Quah</creatorcontrib><creatorcontrib>Quek, C.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kian Hong Quah</au><au>Quek, C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2007-03-01</date><risdate>2007</risdate><volume>18</volume><issue>2</issue><spage>431</spage><epage>448</epage><pages>431-448</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. 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| subjects | Accuracy measure adaptive neurofuzzy inference systems (ANFISs) Adaptive systems Algorithm design and analysis Algorithms Artificial Intelligence audio signal classification automobile miles per gallon (MPG) prediction Automobiles Classification Computational efficiency Computer Simulation Data mining Decision trees Dynamical systems Filters financial data modeling induction algorithm Inference algorithms Information Storage and Retrieval - methods low computational cost Models, Statistical Monte Carlo evaluative feature selection Monte Carlo evaluative selection (MCES) Monte Carlo Method Monte Carlo methods multilayer perceptron (MLP) Neural networks Neural Networks (Computer) Nonlinear dynamics Nonlinear systems Pattern classification Pattern Recognition, Automated - methods regressive series reinforcement learning Sampling methods Studies Tasks |
| title | MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections |
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