Evaluating integrated weight linear method to class imbalanced learning in video data
With the enormous amount of video data especially with the existence of the noisy and irrelevant information, it would be difficult for a typical detection process to capture a small portion of targeted due to the class imbalance problem. In this paper, class imbalance referred to a very small perce...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/69397/ http://psasir.upm.edu.my/id/eprint/69397/1/Evaluating%20integrated%20weight%20linear%20method%20to%20class%20imbalanced%20learning%20in%20video%20data.pdf |
| _version_ | 1848856396074844160 |
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| author | Mohd Apandi, Ziti Fariha Mustapha, Norwati Affendey, Lilly Suriani |
| author_facet | Mohd Apandi, Ziti Fariha Mustapha, Norwati Affendey, Lilly Suriani |
| author_sort | Mohd Apandi, Ziti Fariha |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | With the enormous amount of video data especially with the existence of the noisy and irrelevant information, it would be difficult for a typical detection process to capture a small portion of targeted due to the class imbalance problem. In this paper, class imbalance referred to a very small percentage of positive instance versus negative instances, where the negative instances dominate the detection model, resulting in the degradation of the detection performance. This paper proposed an Integrated Weight Linear (IWL) method that integrate weight linear algorithm (WL) with principle component analysis (PCA) to eliminate imbalanced dataset in soccer video data. PCA is adopted in the first phase with the aim to alleviates the imbalanced data and prepared the reduced instances to the next phase. In the second phase, the reduces instances are refined using the weight linear algorithm. The experiment results using 9 soccer video demonstrate that the integration of PCA and WL is capable to alleviates the imbalanced problem and able to improve classification performance in video data. |
| first_indexed | 2025-11-15T11:40:59Z |
| format | Conference or Workshop Item |
| id | upm-69397 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:40:59Z |
| publishDate | 2011 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-693972019-07-04T04:15:18Z http://psasir.upm.edu.my/id/eprint/69397/ Evaluating integrated weight linear method to class imbalanced learning in video data Mohd Apandi, Ziti Fariha Mustapha, Norwati Affendey, Lilly Suriani With the enormous amount of video data especially with the existence of the noisy and irrelevant information, it would be difficult for a typical detection process to capture a small portion of targeted due to the class imbalance problem. In this paper, class imbalance referred to a very small percentage of positive instance versus negative instances, where the negative instances dominate the detection model, resulting in the degradation of the detection performance. This paper proposed an Integrated Weight Linear (IWL) method that integrate weight linear algorithm (WL) with principle component analysis (PCA) to eliminate imbalanced dataset in soccer video data. PCA is adopted in the first phase with the aim to alleviates the imbalanced data and prepared the reduced instances to the next phase. In the second phase, the reduces instances are refined using the weight linear algorithm. The experiment results using 9 soccer video demonstrate that the integration of PCA and WL is capable to alleviates the imbalanced problem and able to improve classification performance in video data. IEEE 2011 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69397/1/Evaluating%20integrated%20weight%20linear%20method%20to%20class%20imbalanced%20learning%20in%20video%20data.pdf Mohd Apandi, Ziti Fariha and Mustapha, Norwati and Affendey, Lilly Suriani (2011) Evaluating integrated weight linear method to class imbalanced learning in video data. In: 2011 3rd Conference on Data Mining and Optimization (DMO), 28-29 June 2011, Putrajaya, Malaysia. (pp. 243-247). 10.1109/DMO.2011.5976535 |
| spellingShingle | Mohd Apandi, Ziti Fariha Mustapha, Norwati Affendey, Lilly Suriani Evaluating integrated weight linear method to class imbalanced learning in video data |
| title | Evaluating integrated weight linear method to class imbalanced learning in video data |
| title_full | Evaluating integrated weight linear method to class imbalanced learning in video data |
| title_fullStr | Evaluating integrated weight linear method to class imbalanced learning in video data |
| title_full_unstemmed | Evaluating integrated weight linear method to class imbalanced learning in video data |
| title_short | Evaluating integrated weight linear method to class imbalanced learning in video data |
| title_sort | evaluating integrated weight linear method to class imbalanced learning in video data |
| url | http://psasir.upm.edu.my/id/eprint/69397/ http://psasir.upm.edu.my/id/eprint/69397/ http://psasir.upm.edu.my/id/eprint/69397/1/Evaluating%20integrated%20weight%20linear%20method%20to%20class%20imbalanced%20learning%20in%20video%20data.pdf |