Evaluation of Feature Selection Methods and Machine Learning Models for Identifying Collided Positions of Containers Equipped with an Accelerometer Article Swipe
Xin Zhang
,
Zihan Song
,
Do-Myung Park
,
Byung-Kwon Park
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0143.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0143.v1
In the logistics and trade that are highly dependent on containers, efficient identification of col-lided positions is of great significance for enhancing cargo safety supervision and accident respon-sibility. Traditional methods that rely on visual inspections require a lot of manpower, are time-consuming and costly. This study proposes a machine learning-based system to identify col-lided positions using the data collected through accelerometers installed on container doors. This study also uses feature selection techniques to reduce the data dimensionality, thereby improving computational efficiency and reducing computational costs. The feature selection methods evalu-ated include: Pearson Correlation Coefficient, Mutual Information, Sequential Forward Selection, Sequential Backward Selection, and Extreme Tree. The machine learning models evaluated in-clude Decision Tree, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting. Experimental results show that feature selection effectively reduces data di-mensionality and computational cost while maintaining or improving classification accuracy. The best combination of classification model and feature selection method is the combination of K-Nearest Neighbor and Extreme Tree, which achieved the best balance in terms of accuracy 0.9712, execution time 0.0300 seconds and CPU utilization 0.10%. It has a considerably practical significance in the logistics field in terms of safer and more efficient cargo transportation, reliable accident responsibility identification with reduced computing costs.
Related Topics
Concepts
Feature selection
Computer science
Random forest
Support vector machine
Extreme learning machine
Machine learning
Artificial intelligence
SAFER
Decision tree
k-nearest neighbors algorithm
Feature (linguistics)
Curse of dimensionality
Information gain ratio
Data mining
Gradient boosting
Identification (biology)
Philosophy
Artificial neural network
Linguistics
Biology
Computer security
Botany
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202501.0143.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406112546
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406112546Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202501.0143.v1Digital Object Identifier
- Title
-
Evaluation of Feature Selection Methods and Machine Learning Models for Identifying Collided Positions of Containers Equipped with an AccelerometerWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-03Full publication date if available
- Authors
-
Xin Zhang, Zihan Song, Do-Myung Park, Byung-Kwon ParkList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202501.0143.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202501.0143.v1Direct OA link when available
- Concepts
-
Feature selection, Computer science, Random forest, Support vector machine, Extreme learning machine, Machine learning, Artificial intelligence, SAFER, Decision tree, k-nearest neighbors algorithm, Feature (linguistics), Curse of dimensionality, Information gain ratio, Data mining, Gradient boosting, Identification (biology), Philosophy, Artificial neural network, Linguistics, Biology, Computer security, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.feature | 69, 86, 128, 150 |
| abstract_inverted_index.machine | 48, 106 |
| abstract_inverted_index.methods | 29, 88 |
| abstract_inverted_index.reduced | 204 |
| abstract_inverted_index.reduces | 131 |
| abstract_inverted_index.require | 35 |
| abstract_inverted_index.results | 125 |
| abstract_inverted_index.seconds | 175 |
| abstract_inverted_index.thereby | 77 |
| abstract_inverted_index.through | 59 |
| abstract_inverted_index.Backward | 100 |
| abstract_inverted_index.Decision | 111 |
| abstract_inverted_index.Gradient | 122 |
| abstract_inverted_index.Machine, | 117 |
| abstract_inverted_index.Neighbor | 158 |
| abstract_inverted_index.accident | 26, 200 |
| abstract_inverted_index.accuracy | 170 |
| abstract_inverted_index.achieved | 163 |
| abstract_inverted_index.identify | 52 |
| abstract_inverted_index.in-clude | 110 |
| abstract_inverted_index.include: | 90 |
| abstract_inverted_index.learning | 107 |
| abstract_inverted_index.proposes | 46 |
| abstract_inverted_index.reducing | 82 |
| abstract_inverted_index.reliable | 199 |
| abstract_inverted_index.Boosting. | 123 |
| abstract_inverted_index.K-Nearest | 113, 157 |
| abstract_inverted_index.Neighbor, | 114 |
| abstract_inverted_index.accuracy. | 142 |
| abstract_inverted_index.col-lided | 14, 53 |
| abstract_inverted_index.collected | 58 |
| abstract_inverted_index.computing | 205 |
| abstract_inverted_index.container | 63 |
| abstract_inverted_index.dependent | 8 |
| abstract_inverted_index.efficient | 11, 196 |
| abstract_inverted_index.enhancing | 21 |
| abstract_inverted_index.evaluated | 109 |
| abstract_inverted_index.execution | 172 |
| abstract_inverted_index.improving | 78, 140 |
| abstract_inverted_index.installed | 61 |
| abstract_inverted_index.logistics | 2, 188 |
| abstract_inverted_index.manpower, | 39 |
| abstract_inverted_index.positions | 15, 54 |
| abstract_inverted_index.practical | 184 |
| abstract_inverted_index.selection | 70, 87, 129, 151 |
| abstract_inverted_index.Selection, | 98, 101 |
| abstract_inverted_index.Sequential | 96, 99 |
| abstract_inverted_index.efficiency | 80 |
| abstract_inverted_index.evalu-ated | 89 |
| abstract_inverted_index.techniques | 71 |
| abstract_inverted_index.Correlation | 92 |
| abstract_inverted_index.Traditional | 28 |
| abstract_inverted_index.combination | 145, 155 |
| abstract_inverted_index.containers, | 10 |
| abstract_inverted_index.effectively | 130 |
| abstract_inverted_index.inspections | 34 |
| abstract_inverted_index.maintaining | 138 |
| abstract_inverted_index.supervision | 24 |
| abstract_inverted_index.utilization | 178 |
| abstract_inverted_index.Coefficient, | 93 |
| abstract_inverted_index.Experimental | 124 |
| abstract_inverted_index.Information, | 95 |
| abstract_inverted_index.considerably | 183 |
| abstract_inverted_index.significance | 19, 185 |
| abstract_inverted_index.computational | 79, 83, 135 |
| abstract_inverted_index.accelerometers | 60 |
| abstract_inverted_index.classification | 141, 147 |
| abstract_inverted_index.identification | 12, 202 |
| abstract_inverted_index.learning-based | 49 |
| abstract_inverted_index.responsibility | 201 |
| abstract_inverted_index.time-consuming | 41 |
| abstract_inverted_index.di-mensionality | 133 |
| abstract_inverted_index.dimensionality, | 76 |
| abstract_inverted_index.transportation, | 198 |
| abstract_inverted_index.respon-sibility. | 27 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.0065339 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |