Evolutionary support vector regression for monitoring Poisson profiles
<p dir="ltr">Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variable...
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2023
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| _version_ | 1864513530945863680 |
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| author | Ali Yeganeh (16624998) |
| author2 | Saddam Akber Abbasi (7908302) Sandile Charles Shongwe (11986029) Jean-Claude Malela-Majika (10099041) Ali Reza Shadman (16896390) |
| author2_role | author author author author |
| author_facet | Ali Yeganeh (16624998) Saddam Akber Abbasi (7908302) Sandile Charles Shongwe (11986029) Jean-Claude Malela-Majika (10099041) Ali Reza Shadman (16896390) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali Yeganeh (16624998) Saddam Akber Abbasi (7908302) Sandile Charles Shongwe (11986029) Jean-Claude Malela-Majika (10099041) Ali Reza Shadman (16896390) |
| dc.date.none.fl_str_mv | 2023-09-20T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00500-023-09047-2 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Evolutionary_support_vector_regression_for_monitoring_Poisson_profiles/24980814 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Machine learning Mathematical sciences Statistics Control charts Particle swarm optimization Poisson profiles Profile monitoring Statistical process control Support vector regression |
| dc.title.none.fl_str_mv | Evolutionary support vector regression for monitoring Poisson profiles |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variables, known as general linear models which consists of two main categories (i.e., logistic and Poisson profiles). This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. Moreover, the performance of the proposed scheme is further investigated for Poisson profiles with both fixed and random explanatory variables as well as non-parametric profiles. The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. A diagnostic method with machine learning approach is also used to identify the parameters of change in the profile. It is shown that the proposed profile diagnosis approach is able to reach acceptable results in comparison with other competitors. A real-life example in monitoring Poisson profiles is also provided to illustrate the implementation of the proposed charting scheme.</p><h2>Other Information</h2><p dir="ltr">Published in: Soft Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00500-023-09047-2" target="_blank">https://dx.doi.org/10.1007/s00500-023-09047-2</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_65df903ff460c9ff7ac359863603d6eb |
| identifier_str_mv | 10.1007/s00500-023-09047-2 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24980814 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Evolutionary support vector regression for monitoring Poisson profilesAli Yeganeh (16624998)Saddam Akber Abbasi (7908302)Sandile Charles Shongwe (11986029)Jean-Claude Malela-Majika (10099041)Ali Reza Shadman (16896390)Information and computing sciencesMachine learningMathematical sciencesStatisticsControl chartsParticle swarm optimizationPoisson profilesProfile monitoringStatistical process controlSupport vector regression<p dir="ltr">Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variables, known as general linear models which consists of two main categories (i.e., logistic and Poisson profiles). This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. Moreover, the performance of the proposed scheme is further investigated for Poisson profiles with both fixed and random explanatory variables as well as non-parametric profiles. The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. A diagnostic method with machine learning approach is also used to identify the parameters of change in the profile. It is shown that the proposed profile diagnosis approach is able to reach acceptable results in comparison with other competitors. A real-life example in monitoring Poisson profiles is also provided to illustrate the implementation of the proposed charting scheme.</p><h2>Other Information</h2><p dir="ltr">Published in: Soft Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00500-023-09047-2" target="_blank">https://dx.doi.org/10.1007/s00500-023-09047-2</a></p>2023-09-20T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00500-023-09047-2https://figshare.com/articles/journal_contribution/Evolutionary_support_vector_regression_for_monitoring_Poisson_profiles/24980814CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249808142023-09-20T03:00:00Z |
| spellingShingle | Evolutionary support vector regression for monitoring Poisson profiles Ali Yeganeh (16624998) Information and computing sciences Machine learning Mathematical sciences Statistics Control charts Particle swarm optimization Poisson profiles Profile monitoring Statistical process control Support vector regression |
| status_str | publishedVersion |
| title | Evolutionary support vector regression for monitoring Poisson profiles |
| title_full | Evolutionary support vector regression for monitoring Poisson profiles |
| title_fullStr | Evolutionary support vector regression for monitoring Poisson profiles |
| title_full_unstemmed | Evolutionary support vector regression for monitoring Poisson profiles |
| title_short | Evolutionary support vector regression for monitoring Poisson profiles |
| title_sort | Evolutionary support vector regression for monitoring Poisson profiles |
| topic | Information and computing sciences Machine learning Mathematical sciences Statistics Control charts Particle swarm optimization Poisson profiles Profile monitoring Statistical process control Support vector regression |