کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408840 | 679042 | 2009 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Meta-learning for imbalanced data and classification ensemble in binary classification
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Meta-learning for imbalanced data and classification ensemble in binary classification Meta-learning for imbalanced data and classification ensemble in binary classification](/preview/png/408840.png)
چکیده انگلیسی
To conduct binary classification with highly imbalanced data is a very common problem, especially when the examples of interest are relatively rare. In this paper, we proposed the “Meta Imbalanced Classification Ensemble (MICE)” algorithm in order to dilute the effect of imbalanced data. In the MICE, the majority group is partitioned based on the transformed features from “inner product” to retain the geometric relation between two groups. The empirical results show that the performance of MICE is better than some renowned classification methods in terms of the specificity and the sensitivity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 73, Issues 1–3, December 2009, Pages 484–494
Journal: Neurocomputing - Volume 73, Issues 1–3, December 2009, Pages 484–494
نویسندگان
Sung-Chiang Lin, Yuan-chin I. Chang, Wei-Ning Yang,