การใส่ค่าข้อมูลที่ขาดหายโดยวิธีเพื่อนบ้านใกล้ที่สุดเคตัวเพื่อการจำแนกประเภทในชุดข้อมูลอสมดุล
Class imbalance is a problem that aims to improve the accuracy of a minority class, while imputation is a process to replace missing values. Traditionally, class imbalance and imputation problems are considered independently. In addition, filled-in minority-class values that are substituted by tradi...
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| Format: | Theses and Dissertations |
| Language: | Thai |
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เชียงใหม่ : บัณฑิตวิทยาลัย มหาวิทยาลัยเชียงใหม่
2020
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| Online Access: | http://cmuir.cmu.ac.th/jspui/handle/6653943832/69257 |
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| Institution: | Chiang Mai University |
| Language: | Thai |
| Summary: | Class imbalance is a problem that aims to improve the accuracy of a minority class, while imputation is a process to replace missing values. Traditionally, class imbalance and imputation problems are considered independently. In addition, filled-in minority-class values that are substituted by traditional methods are not sufficient for imbalance datasets. In this paper, we provide a new parameter-free imputation to operate on imbalance datasets by estimating a random value between the mean of the missing value attribute and a value in this attribute of the closet record instance from the missing value record. Our proposed algorithm ignores mean of instances to avoid an over-fitting problem. Consequently, experimental results on imbalance datasets reveal that our imputation outperforms other techniques, when class imbalance measures are used. |
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