Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem
Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predicti...
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MDPI AG
2022-01-01
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| Series: | Mathematics |
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| author | Salam Salameh Shreem Mohd Zakree Ahmad Nazri Salwani Abdullah Nor Samsiah Sani |
| author_facet | Salam Salameh Shreem Mohd Zakree Ahmad Nazri Salwani Abdullah Nor Samsiah Sani |
| author_sort | Salam Salameh Shreem |
| collection | DOAJ |
| description | Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy. |
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| format | Article |
| id | doaj.art-9f68914c2eb5453a9fbdcb58dd5c6f03 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T23:33:22Z |
| publishDate | 2022-01-01 |
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| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-9f68914c2eb5453a9fbdcb58dd5c6f032023-11-23T17:06:28ZengMDPI AGMathematics2227-73902022-01-0110337410.3390/math10030374Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection ProblemSalam Salameh Shreem0Mohd Zakree Ahmad Nazri1Salwani Abdullah2Nor Samsiah Sani3HLT Service Group Inc., 5818 S Archer Rd Suit 111, Summit, IL 60501, USACentre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaCentre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaCentre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaSelecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy.https://www.mdpi.com/2227-7390/10/3/374symmetrical uncertaintyreference setharmony search algorithmgene selection |
| spellingShingle | Salam Salameh Shreem Mohd Zakree Ahmad Nazri Salwani Abdullah Nor Samsiah Sani Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem Mathematics symmetrical uncertainty reference set harmony search algorithm gene selection |
| title | Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem |
| title_full | Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem |
| title_fullStr | Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem |
| title_full_unstemmed | Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem |
| title_short | Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem |
| title_sort | hybrid symmetrical uncertainty and reference set harmony search algorithm for gene selection problem |
| topic | symmetrical uncertainty reference set harmony search algorithm gene selection |
| url | https://www.mdpi.com/2227-7390/10/3/374 |
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