Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling
In this paper, we present a comparative analysis of Synthetic Minority Oversampling TEchnique (SMOTE) and Random OverSampling Examples (ROSE) oversampling techniques for K Nearest Neigbhors KNN-based autonomous vehicle behavior modeling. We address the challenges posed by imbalanced and mixed datase...
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| Format: | Article |
| Language: | English |
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Croatian Communications and Information Society (CCIS)
2025-03-01
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| Series: | Journal of Communications Software and Systems |
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| Online Access: | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0121/ |
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| _version_ | 1850155566565949440 |
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| author | Celine Serbouh Touazi Iness Ahriz Ndeye Niang Alain Piperno |
| author_facet | Celine Serbouh Touazi Iness Ahriz Ndeye Niang Alain Piperno |
| author_sort | Celine Serbouh Touazi |
| collection | DOAJ |
| description | In this paper, we present a comparative analysis of Synthetic Minority Oversampling TEchnique (SMOTE) and Random OverSampling Examples (ROSE) oversampling techniques for K Nearest Neigbhors KNN-based autonomous vehicle behavior modeling. We address the challenges posed by imbalanced and mixed datasets in the context of autonomous vehicle testing, where the majority of test outcomes are classified as ”OK” (safe) and fewer as ”KO” (unsafe). We propose an enhanced approach that extends our previous work by incorporating ROSE as an alternative to SMOTE for generating synthetic samples. We integrate these resampling techniques with Leave- One-Out Cross-Validation (LOO-CV), applying resampling at each iteration to ensure data balancing is tailored to each training set. Additionally, we investigate the impact of different encoding strategies for categorical variables, including OneHot, binary encoding, and Factor Analysis of Mixed Data (FAMD). Our research aims to develop a robust classification model capable of accurately predicting autonomous vehicle behavior while effectively managing class imbalance and mixed data types, despite the limited availability of data due to costly and timeconsuming testing procedures. |
| format | Article |
| id | doaj-art-8cdaa2da4e42447fa0c35c1fd1b63fbd |
| institution | OA Journals |
| issn | 1845-6421 1846-6079 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Croatian Communications and Information Society (CCIS) |
| record_format | Article |
| series | Journal of Communications Software and Systems |
| spelling | doaj-art-8cdaa2da4e42447fa0c35c1fd1b63fbd2025-08-20T02:24:51ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792025-03-0121213214310.24138/jcomss-2024-0121Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior ModelingCeline Serbouh TouaziIness AhrizNdeye NiangAlain PipernoIn this paper, we present a comparative analysis of Synthetic Minority Oversampling TEchnique (SMOTE) and Random OverSampling Examples (ROSE) oversampling techniques for K Nearest Neigbhors KNN-based autonomous vehicle behavior modeling. We address the challenges posed by imbalanced and mixed datasets in the context of autonomous vehicle testing, where the majority of test outcomes are classified as ”OK” (safe) and fewer as ”KO” (unsafe). We propose an enhanced approach that extends our previous work by incorporating ROSE as an alternative to SMOTE for generating synthetic samples. We integrate these resampling techniques with Leave- One-Out Cross-Validation (LOO-CV), applying resampling at each iteration to ensure data balancing is tailored to each training set. Additionally, we investigate the impact of different encoding strategies for categorical variables, including OneHot, binary encoding, and Factor Analysis of Mixed Data (FAMD). Our research aims to develop a robust classification model capable of accurately predicting autonomous vehicle behavior while effectively managing class imbalance and mixed data types, despite the limited availability of data due to costly and timeconsuming testing procedures.https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0121/iaautonomous vehiclessmoterose |
| spellingShingle | Celine Serbouh Touazi Iness Ahriz Ndeye Niang Alain Piperno Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling Journal of Communications Software and Systems ia autonomous vehicles smote rose |
| title | Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling |
| title_full | Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling |
| title_fullStr | Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling |
| title_full_unstemmed | Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling |
| title_short | Comparative Analysis of SMOTE and ROSE Oversampling Techniques for kNN-Based Autonomous Vehicle Behavior Modeling |
| title_sort | comparative analysis of smote and rose oversampling techniques for knn based autonomous vehicle behavior modeling |
| topic | ia autonomous vehicles smote rose |
| url | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0121/ |
| work_keys_str_mv | AT celineserbouhtouazi comparativeanalysisofsmoteandroseoversamplingtechniquesforknnbasedautonomousvehiclebehaviormodeling AT inessahriz comparativeanalysisofsmoteandroseoversamplingtechniquesforknnbasedautonomousvehiclebehaviormodeling AT ndeyeniang comparativeanalysisofsmoteandroseoversamplingtechniquesforknnbasedautonomousvehiclebehaviormodeling AT alainpiperno comparativeanalysisofsmoteandroseoversamplingtechniquesforknnbasedautonomousvehiclebehaviormodeling |