Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization
In order to classify data and improve extreme learning machine (ELM), this study explains how a hybrid optimization-driven ELM technique was devised. Input data are pre-processed in order to compute missing values and convert data to numerical values using the exponential kernel transform. The Jaro–...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-10-01
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| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2022-0257 |
| _version_ | 1797989280113491968 |
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| author | Rathod Nilesh Wankhade Sunil |
| author_facet | Rathod Nilesh Wankhade Sunil |
| author_sort | Rathod Nilesh |
| collection | DOAJ |
| description | In order to classify data and improve extreme learning machine (ELM), this study explains how a hybrid optimization-driven ELM technique was devised. Input data are pre-processed in order to compute missing values and convert data to numerical values using the exponential kernel transform. The Jaro–Winkler distance is used to identify the relevant features. The feed-forward neural network classifier is used to categorize the data, and it is trained using a hybrid optimization technique called the modified enhanced Invasive Weed, a meta heuristic algorithm, and Cuckoo Search, a non-linear optimization algorithm ELM. The enhanced Invasive Weed optimization (IWO) algorithm and the enhanced Cuckoo Search (CS) algorithm are combined to create the modified CSIWO. The experimental findings presented in this work demonstrate the viability and efficacy of the created ELM method based on CSIWO, with good experimental result as compared to other ELM techniques. |
| first_indexed | 2024-04-11T08:17:44Z |
| format | Article |
| id | doaj.art-1fcf479fd51549f9b03dadc4334356ba |
| institution | Directory Open Access Journal |
| issn | 2192-8029 |
| language | English |
| last_indexed | 2024-04-11T08:17:44Z |
| publishDate | 2022-10-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj.art-1fcf479fd51549f9b03dadc4334356ba2022-12-22T04:35:04ZengDe GruyterNonlinear Engineering2192-80292022-10-0111156858110.1515/nleng-2022-0257Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimizationRathod Nilesh0Wankhade Sunil1Department of Computer Engineering, Mct’s RGIT, Andheri, Mumbai, 400053, IndiaDepartment of Information Technology, Mct’s RGIT, Andheri, Mumbai, 400053, IndiaIn order to classify data and improve extreme learning machine (ELM), this study explains how a hybrid optimization-driven ELM technique was devised. Input data are pre-processed in order to compute missing values and convert data to numerical values using the exponential kernel transform. The Jaro–Winkler distance is used to identify the relevant features. The feed-forward neural network classifier is used to categorize the data, and it is trained using a hybrid optimization technique called the modified enhanced Invasive Weed, a meta heuristic algorithm, and Cuckoo Search, a non-linear optimization algorithm ELM. The enhanced Invasive Weed optimization (IWO) algorithm and the enhanced Cuckoo Search (CS) algorithm are combined to create the modified CSIWO. The experimental findings presented in this work demonstrate the viability and efficacy of the created ELM method based on CSIWO, with good experimental result as compared to other ELM techniques.https://doi.org/10.1515/nleng-2022-0257optimizationneural networkinvasive weed optimizationextreme learning machine algorithmcuckoo search optimizationlinear function |
| spellingShingle | Rathod Nilesh Wankhade Sunil Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization Nonlinear Engineering optimization neural network invasive weed optimization extreme learning machine algorithm cuckoo search optimization linear function |
| title | Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization |
| title_full | Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization |
| title_fullStr | Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization |
| title_full_unstemmed | Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization |
| title_short | Investigation of optimized ELM using Invasive Weed-optimization and Cuckoo-Search optimization |
| title_sort | investigation of optimized elm using invasive weed optimization and cuckoo search optimization |
| topic | optimization neural network invasive weed optimization extreme learning machine algorithm cuckoo search optimization linear function |
| url | https://doi.org/10.1515/nleng-2022-0257 |
| work_keys_str_mv | AT rathodnilesh investigationofoptimizedelmusinginvasiveweedoptimizationandcuckoosearchoptimization AT wankhadesunil investigationofoptimizedelmusinginvasiveweedoptimizationandcuckoosearchoptimization |