Results of machine learning models on Ca-Land.
<div><p>In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affecte...
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2025
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| _version_ | 1852016519104430080 |
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| author | Yulin Li (36434) |
| author_facet | Yulin Li (36434) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yulin Li (36434) |
| dc.date.none.fl_str_mv | 2025-09-17T17:38:40Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332439.g017 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Results_of_machine_learning_models_on_Ca-Land_/30151622 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological Sciences not elsewhere classified Information Systems not elsewhere classified thus metaheuristic algorithms swarm intelligence algorithms reduced population diversity post epidemic era local optimal solutions indicators fully proves global economic austerity five evaluation metrics fitness allocation strategy elite evolution mechanism cauchy variation strategy become particularly important basic human needs 66 %, 18 27 %, 28 10 %, 49 bp neural network bpnn improved 17 network parameters xlink "> widely used turn results slow convergence results show prediction tasks paper proposes optimization tasks house prices four datasets first compared easily affected accurate prediction |
| dc.title.none.fl_str_mv | Results of machine learning models on Ca-Land. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affected by weights and biases, and thus metaheuristic algorithms are needed to optimize the network parameters. Firstly, to address the shortcomings of the artificial Gorilla Troops Optimizer (GTO) in such optimization tasks, such as reduced population diversity, easy to fall into local optimal solutions and slow convergence, this paper proposes a fitness allocation strategy, a Cauchy variation strategy, and an elite evolution mechanism to improve the algorithm, which in turn results in an improved artificial Gorilla Troops Optimizer (IGTO). Subsequently, a BP neural network house price prediction model based on IGTO is constructed and experiments are conducted on four datasets, namely, Boston, California-Bay, California-Land and Taiwan. The experiments are first compared with eleven other swarm intelligence algorithms and then with four machine learning models, and the results show that IGTO-BPNN improved 17.66%, 18.27%, 28.10%, 49.35% and 24.83% on five evaluation metrics, namely, MAE, MAPE, R2, RMSE, and SMAPE, respectively. The improvement of these indicators fully proves the superiority and effectiveness of IGTO-BPNN in house price prediction.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_a8317e1caca0236dedf2f8ca2e869fed |
| identifier_str_mv | 10.1371/journal.pone.0332439.g017 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30151622 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Results of machine learning models on Ca-Land.Yulin Li (36434)Biological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthus metaheuristic algorithmsswarm intelligence algorithmsreduced population diversitypost epidemic eralocal optimal solutionsindicators fully provesglobal economic austerityfive evaluation metricsfitness allocation strategyelite evolution mechanismcauchy variation strategybecome particularly importantbasic human needs66 %, 1827 %, 2810 %, 49bp neural networkbpnn improved 17network parametersxlink ">widely usedturn resultsslow convergenceresults showprediction taskspaper proposesoptimization taskshouse pricesfour datasetsfirst comparedeasily affectedaccurate prediction<div><p>In the context of global economic austerity in the post epidemic era, housing, as one of the basic human needs, has become particularly important for accurate prediction of house prices. BP neural network is widely used in prediction tasks, but their performance is easily affected by weights and biases, and thus metaheuristic algorithms are needed to optimize the network parameters. Firstly, to address the shortcomings of the artificial Gorilla Troops Optimizer (GTO) in such optimization tasks, such as reduced population diversity, easy to fall into local optimal solutions and slow convergence, this paper proposes a fitness allocation strategy, a Cauchy variation strategy, and an elite evolution mechanism to improve the algorithm, which in turn results in an improved artificial Gorilla Troops Optimizer (IGTO). Subsequently, a BP neural network house price prediction model based on IGTO is constructed and experiments are conducted on four datasets, namely, Boston, California-Bay, California-Land and Taiwan. The experiments are first compared with eleven other swarm intelligence algorithms and then with four machine learning models, and the results show that IGTO-BPNN improved 17.66%, 18.27%, 28.10%, 49.35% and 24.83% on five evaluation metrics, namely, MAE, MAPE, R2, RMSE, and SMAPE, respectively. The improvement of these indicators fully proves the superiority and effectiveness of IGTO-BPNN in house price prediction.</p></div>2025-09-17T17:38:40ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332439.g017https://figshare.com/articles/figure/Results_of_machine_learning_models_on_Ca-Land_/30151622CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301516222025-09-17T17:38:40Z |
| spellingShingle | Results of machine learning models on Ca-Land. Yulin Li (36434) Biological Sciences not elsewhere classified Information Systems not elsewhere classified thus metaheuristic algorithms swarm intelligence algorithms reduced population diversity post epidemic era local optimal solutions indicators fully proves global economic austerity five evaluation metrics fitness allocation strategy elite evolution mechanism cauchy variation strategy become particularly important basic human needs 66 %, 18 27 %, 28 10 %, 49 bp neural network bpnn improved 17 network parameters xlink "> widely used turn results slow convergence results show prediction tasks paper proposes optimization tasks house prices four datasets first compared easily affected accurate prediction |
| status_str | publishedVersion |
| title | Results of machine learning models on Ca-Land. |
| title_full | Results of machine learning models on Ca-Land. |
| title_fullStr | Results of machine learning models on Ca-Land. |
| title_full_unstemmed | Results of machine learning models on Ca-Land. |
| title_short | Results of machine learning models on Ca-Land. |
| title_sort | Results of machine learning models on Ca-Land. |
| topic | Biological Sciences not elsewhere classified Information Systems not elsewhere classified thus metaheuristic algorithms swarm intelligence algorithms reduced population diversity post epidemic era local optimal solutions indicators fully proves global economic austerity five evaluation metrics fitness allocation strategy elite evolution mechanism cauchy variation strategy become particularly important basic human needs 66 %, 18 27 %, 28 10 %, 49 bp neural network bpnn improved 17 network parameters xlink "> widely used turn results slow convergence results show prediction tasks paper proposes optimization tasks house prices four datasets first compared easily affected accurate prediction |