Minimum Displacement in Existing Moment (MDEM)- A new supervised learning algorithm by incrementally constructing the moments of the underlying classes
We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the class's existing n-th central moment. After each such inclusion, the n-th central moment of the corresponding class is updated by some incr...
Gespeichert in:
| Veröffentlicht in: | PloS one Jg. 20; H. 12; S. e0336933 |
|---|---|
| 1. Verfasser: | |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Public Library of Science
01.12.2025
|
| Schlagworte: | |
| ISSN: | 1932-6203 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the class's existing n-th central moment. After each such inclusion, the n-th central moment of the corresponding class is updated by some incremental calculations in constant time, i.e., each class evolves gradually and changes its definition incrementally after the inclusion of every new data point. We then use k-fold and stratified k-fold cross validation techniques to compare the performance of our proposed model with various state of the art supervised learning algorithms including Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Logistic Regression (LR) using Pima Indian Diabetes (PID) dataset and Wisconsin breast cancer dataset, which are popular datasets in machine learning research. Our analyses suggest the performances of different MDEM algorithms as proposed here involving different order of moments vary within the range of [83.19% - 95.82%] of the best algorithm under consideration in k-fold and stratified k-fold cross validation techniques for PID dataset. Moreover, for Wisconsin breast cancer dataset, different variants of MDEM algorithms have achieved accuracy scores in the range of [88.85% - 96.41%] of the best algorithm. Finally, we compare the results produced by different algorithms by constructing the corresponding confusion matrices. |
|---|---|
| AbstractList | We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the class's existing n-th central moment. After each such inclusion, the n-th central moment of the corresponding class is updated by some incremental calculations in constant time, i.e., each class evolves gradually and changes its definition incrementally after the inclusion of every new data point. We then use k-fold and stratified k-fold cross validation techniques to compare the performance of our proposed model with various state of the art supervised learning algorithms including Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Logistic Regression (LR) using Pima Indian Diabetes (PID) dataset and Wisconsin breast cancer dataset, which are popular datasets in machine learning research. Our analyses suggest the performances of different MDEM algorithms as proposed here involving different order of moments vary within the range of [83.19% - 95.82%] of the best algorithm under consideration in k-fold and stratified k-fold cross validation techniques for PID dataset. Moreover, for Wisconsin breast cancer dataset, different variants of MDEM algorithms have achieved accuracy scores in the range of [88.85% - 96.41%] of the best algorithm. Finally, we compare the results produced by different algorithms by constructing the corresponding confusion matrices. We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the class’s existing n-th central moment. After each such inclusion, the n-th central moment of the corresponding class is updated by some incremental calculations in constant time, i.e., each class evolves gradually and changes its definition incrementally after the inclusion of every new data point. We then use k-fold and stratified k-fold cross validation techniques to compare the performance of our proposed model with various state of the art supervised learning algorithms including Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Logistic Regression (LR) using Pima Indian Diabetes (PID) dataset and Wisconsin breast cancer dataset, which are popular datasets in machine learning research. Our analyses suggest the performances of different MDEM algorithms as proposed here involving different order of moments vary within the range of [83.19% − 95.82%] of the best algorithm under consideration in k-fold and stratified k-fold cross validation techniques for PID dataset. Moreover, for Wisconsin breast cancer dataset, different variants of MDEM algorithms have achieved accuracy scores in the range of [88.85% − 96.41%] of the best algorithm. Finally, we compare the results produced by different algorithms by constructing the corresponding confusion matrices. |
| Author | Nizam, Ahmed Mehedi |
| Author_xml | – sequence: 1 givenname: Ahmed Mehedi orcidid: 0000-0002-6929-863X surname: Nizam fullname: Nizam, Ahmed Mehedi organization: The Central Bank of Bangladesh, Motijheel, Dhaka, Bangladesh |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41348909$$D View this record in MEDLINE/PubMed |
| BookMark | eNo1kM1OwzAQhC0Eoj_wBgh8hEOK002c5Fi15UdqxaX3yrG3rSvHiewEyJPwuqShnFY7M_qkmRG5tKVFQu5CNgkhCZ-PZeOsMJOqkycMgGcAF2QYZjAN-JTBgIy8PzIWQ8r5NRlEIURpxrIh-Vlrq4umoAvtKyMkFmhrqi1dfmtfa7un67KXHteL5fopoDNq8Yv6pkL3qT0qalA4ewoKsy-drg8FzduOIF3PEsa0VJbW166RPbA-IC16qKflrn8bq9CZ9uRKI7xHf0OudsJ4vD3fMdm8LDfzt2D18fo-n62CqqsYAO5SofIMFSJTsRQiAgwZhHkmo4hLkcs45olUrCuMXKgEZQoSuIAw2aGCMXn4w1am9NvzjH4L0yRL45Rx1iXuz4kmL1BtK6cL4drt_4TwC0rTeYA |
| ContentType | Journal Article |
| Copyright | Copyright: © 2025 Ahmed Mehedi Nizam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2025 Ahmed Mehedi Nizam. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: Copyright: © 2025 Ahmed Mehedi Nizam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: 2025 Ahmed Mehedi Nizam. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | CGR CUY CVF ECM EIF NPM |
| DOI | 10.1371/journal.pone.0336933 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) |
| DatabaseTitleList | MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 1932-6203 |
| ExternalDocumentID | 3279858060 41348909 |
| Genre | Journal Article |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE ABDBF ABIVO ABJCF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV ADCSY ADRAZ AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AGGLG AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAIFH BAWUL BBNVY BBTPI BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CGR CS3 CUY CVF D1I D1J D1K DIK DU5 E3Z EAP EAS EBD ECM EIF EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPNFZ IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ NPM O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RIG RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM |
| ID | FETCH-LOGICAL-p933-3ef8adb9edee0d5caa43e1031b9c446cabc5567cd0134e6ad7ec83c36a317fed3 |
| IEDL.DBID | FPL |
| IngestDate | Wed Dec 10 15:00:44 EST 2025 Wed Dec 10 15:01:09 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | Copyright: © 2025 Ahmed Mehedi Nizam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-p933-3ef8adb9edee0d5caa43e1031b9c446cabc5567cd0134e6ad7ec83c36a317fed3 |
| ORCID | 0000-0002-6929-863X |
| OpenAccessLink | http://dx.doi.org/10.1371/journal.pone.0336933 |
| PMID | 41348909 |
| ParticipantIDs | plos_journals_3279858060 pubmed_primary_41348909 |
| PublicationCentury | 2000 |
| PublicationDate | 20251201 |
| PublicationDateYYYYMMDD | 2025-12-01 |
| PublicationDate_xml | – month: 12 year: 2025 text: 20251201 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2025 |
| Publisher | Public Library of Science |
| Publisher_xml | – name: Public Library of Science |
| SSID | ssj0053866 |
| Score | 2.4934793 |
| Snippet | We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the... |
| SourceID | plos pubmed |
| SourceType | Open Website Index Database |
| StartPage | e0336933 |
| SubjectTerms | Algorithms Breast cancer Breast Neoplasms Classification Data points Datasets Decision trees Diabetes Mellitus Expected values Female Humans Logistic Models Machine learning Neural networks Neural Networks, Computer Optimization techniques Random variables Regression analysis Sample size Supervised learning Supervised Machine Learning Support Vector Machine Support vector machines |
| Title | Minimum Displacement in Existing Moment (MDEM)- A new supervised learning algorithm by incrementally constructing the moments of the underlying classes |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/41348909 http://dx.doi.org/10.1371/journal.pone.0336933 |
| Volume | 20 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals databaseCode: DOA dateStart: 20060101 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: false ssIdentifier: ssj0053866 providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources databaseCode: M~E dateStart: 20060101 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://road.issn.org omitProxy: false ssIdentifier: ssj0053866 providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database databaseCode: P5Z dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://search.proquest.com/hightechjournals omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Agriculture Science Database databaseCode: M0K dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://search.proquest.com/agriculturejournals omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database databaseCode: M7P dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: http://search.proquest.com/biologicalscijournals omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database databaseCode: M7S dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: http://search.proquest.com omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database databaseCode: PATMY dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: http://search.proquest.com/environmentalscience omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection databaseCode: 7X7 dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://search.proquest.com/healthcomplete omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database databaseCode: KB. dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: http://search.proquest.com/materialsscijournals omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database databaseCode: 7RV dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://search.proquest.com/nahs omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central databaseCode: BENPR dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://www.proquest.com/central omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database databaseCode: 8C1 dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: https://search.proquest.com/publichealth omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database databaseCode: PIMPY dateStart: 20061201 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: http://search.proquest.com/publiccontent omitProxy: false ssIdentifier: ssj0053866 providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access databaseCode: FPL dateStart: 20060101 customDbUrl: isFulltext: true eissn: 1932-6203 dateEnd: 99991231 titleUrlDefault: http://www.plos.org/publications/ omitProxy: false ssIdentifier: ssj0053866 providerName: Public Library of Science |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3BTtwwELUK9MClZWkptGU1Bw5wCE3ijRMfadlVD7urVcWB28qxx0ukbLJaA4Iv4XcZe4MqIXHgmCgeJfMynje2Z4axE_IaRknNI5soGw0QRSSzkkdFogtLkZxMMCA9zqfT4vpazv4Hiq928Hme_Op0er5qGzyPORcUgm-xnZQL4Vs1jGbjl5mXbFeILj3urZG-hmndulckMjiT0ef3vsYe-9TRRrjY4NxjH7DZZ73OMB2cdtWjz76wp0nVVMu7JVxWLhy48st_UDUwfPDm3Cxg0oZbpxPC4CyCCyBmDe5u5WcNhwa6RhILUPWiXVe3N0soH0mC3iwlqrp-BN2-VJ6l54hDwjIIddDacOlT09a1T6EC7fk5uq_sajS8-vM36rovRCv6tIijLZQpJRrE2GRaqQFH3xOilJpCSK1KnWUi14Y45ACFMjnqgmsuFDESi4YfsO2GdHXIgHPCvCSeklgKvmSqEmmULlKrM24RkyN26DGZdyp2c57mssiKWMRH7NsGp_lqU3xjTk6XZMTy-9uDfrDd1HfpDYdOfrJtUgces4_6_rZy6z7b-T2czv71Q_TdDz_QM0XXyAw |
| linkProvider | Public Library of Science |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LU9swENa0lBm4FCgUwqPdQw9wcGpH8UNHBsjQaZLhkAM3jyytgmccOxOHTvkl_busZOXCDLfe_JBW9q4e30r7YOwHrRpaCsUDE0kTDBGTQMQFD7JIZYY0ORGhk_Q4nU6zx0fx4EMKWV8Yz0HSEaumdSf59qKp8afnZHdw2o94Gm0K95f0vh9ynpB2_pF9SkkFsdZdo4fxZlKmYZ0k3nPuvZo2vCm19QZfunVmtPcfv3CfffZgE667GgfsA9Zf2IEfzi1c-pjTV4fs36Ssy8XzAm7L1plp2U1DKGu4-2sngXoOk8Y9upyQ5K4CuAbC49A-L-1c06IGn35iDrKaN6ty_bSA4oUoqG4DUlbVC6hmE6-WyhHyhIUj2kJj3K11aFtV1vEKlEX12B6x2ehudnMf-JwNwZJ-LeBoMqkLgRox1LGScsjRZpIohCLFU8lCxXGSKk3Ic4iJ1CmqjCueSMIxBjX_yrZq4tUJA86ppxSEbiJDKpsYyEhoqbKBUTE3iFGPnVjG5xt55HyQiizOwiTsseNOhPmyC9mR01JNNEJx-n6l72znfjYZ5-Nf099nbHdg8_w6s5VztkWswQu2rf6sy3b1zfWsVyD43nI |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07b9swED6kaVF0SZu-kj5vKIpkkCOZ1oNj0MRoUdvwkCFLIVDk0REgS4blFMkv6d_tkZKWAhm7SQJ5ku74-D6SdwfwhWcNo6QWgY2UDSZESSDjQgRZpDPLTE5G5C09SxeL7PpaLvfg1-AL02uQOWLVtH4n3100NZ31mjxz8Yq63dNRJNJoqDHacKFRKETCFP2rjzjkVsZ2zgHpETxOmZY4bjZdzoaBmrt6kvTedA8JciFP-f3_YE4_90yf_-evfgEHPSjF807KIexR_RIO-27f4kkfm_r0FfyZl3W5vl3jRdn641xucRHLGi_v3GBRr3De-Ecnc7bwaYDnyLgd29uNG5NaMtinqVihqlbNttzdrLG4Zwm6W6hUVXWPuhni2nI5Rqi49kJbbKy_dY5v28o5aKF26J_a13A1vbz69j3oczsEG_7TQJDNlCkkGaLQxFqpiSCXcaKQmgmqVoWO4yTVhhHqhBJlUtKZ0CJRjHcsGfEG9mtW3RGgENyiCkZBkWVqJ8cqkkbpbGx1LCxRdAxHzhj5YKNcjFOZxVmYhMfwtjNrvulCe-Q8pbOMUL57uNJneLq8mOazH4uf7-HZ2KUD9qdbPsA-a4Y-whP9e1e220--sf0FfMntpw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Minimum+Displacement+in+Existing+Moment+%28MDEM%29-+A+new+supervised+learning+algorithm+by+incrementally+constructing+the+moments+of+the+underlying+classes&rft.jtitle=PloS+one&rft.au=Nizam%2C+Ahmed&rft.date=2025-12-01&rft.pub=Public+Library+of+Science&rft.eissn=1932-6203&rft.volume=20&rft.issue=12&rft_id=info:doi/10.1371%2Fjournal.pone.0336933&rft.externalDocID=3279858060 |