Multiple correspondence analysis for handling large binary variables in smoothed location model
Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously.This model is introduced to handle the problem of some empty cells due to the increasing of binary variables.However, smoothed location model i...
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| Format: | Article |
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IP Publishing LLC
2015
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| _version_ | 1803628185899761664 |
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| author | Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina |
| author_facet | Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina |
| author_sort | Ngu, Penny Ai Huong |
| collection | UUM |
| description | Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously.This model is introduced to handle the problem of some empty cells due to the increasing of binary variables.However, smoothed location model is infeasible if involve large number of binary variables.Therefore, the combination of two variable extraction approaches, principal component analysis and multiple correspondence analysis are carried out before the construction of smoothed location model in order to extract large number of measured variables in the study.In fact, there are four types of multiple correspondence analysis but only Burt matrix multiple correspondence analysis had been applied in the latest investigation. Thus, this study aims to examine and compare principal component analysis with four types of multiple correspondence analysis and hope to have better results for data with large number of mixed variables.The proposed model is expected to provide a better or at least comparable classification performance as comparing to others classification methods. |
| first_indexed | 2024-07-04T06:17:57Z |
| format | Article |
| id | uum-21575 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:17:57Z |
| publishDate | 2015 |
| publisher | IP Publishing LLC |
| record_format | dspace |
| spelling | uum-215752017-04-16T04:16:19Z https://repo.uum.edu.my/id/eprint/21575/ Multiple correspondence analysis for handling large binary variables in smoothed location model Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina QA Mathematics Smoothed location model is a discriminant analysis which can be used to handle the data involving mixtures of continuous and binary variables simultaneously.This model is introduced to handle the problem of some empty cells due to the increasing of binary variables.However, smoothed location model is infeasible if involve large number of binary variables.Therefore, the combination of two variable extraction approaches, principal component analysis and multiple correspondence analysis are carried out before the construction of smoothed location model in order to extract large number of measured variables in the study.In fact, there are four types of multiple correspondence analysis but only Burt matrix multiple correspondence analysis had been applied in the latest investigation. Thus, this study aims to examine and compare principal component analysis with four types of multiple correspondence analysis and hope to have better results for data with large number of mixed variables.The proposed model is expected to provide a better or at least comparable classification performance as comparing to others classification methods. IP Publishing LLC 2015 Article PeerReviewed Ngu, Penny Ai Huong and Hamid, Hashibah and Aziz, Nazrina (2015) Multiple correspondence analysis for handling large binary variables in smoothed location model. AIP Conference Proceedings, 1691 (1). 050018. ISSN 0094-243X http://doi.org/10.1063/1.4937100 doi:10.1063/1.4937100 doi:10.1063/1.4937100 |
| spellingShingle | QA Mathematics Ngu, Penny Ai Huong Hamid, Hashibah Aziz, Nazrina Multiple correspondence analysis for handling large binary variables in smoothed location model |
| title | Multiple correspondence analysis for handling large binary variables in smoothed location model |
| title_full | Multiple correspondence analysis for handling large binary variables in smoothed location model |
| title_fullStr | Multiple correspondence analysis for handling large binary variables in smoothed location model |
| title_full_unstemmed | Multiple correspondence analysis for handling large binary variables in smoothed location model |
| title_short | Multiple correspondence analysis for handling large binary variables in smoothed location model |
| title_sort | multiple correspondence analysis for handling large binary variables in smoothed location model |
| topic | QA Mathematics |
| work_keys_str_mv | AT ngupennyaihuong multiplecorrespondenceanalysisforhandlinglargebinaryvariablesinsmoothedlocationmodel AT hamidhashibah multiplecorrespondenceanalysisforhandlinglargebinaryvariablesinsmoothedlocationmodel AT aziznazrina multiplecorrespondenceanalysisforhandlinglargebinaryvariablesinsmoothedlocationmodel |