NDLI: Detect Rare Events via MICE Algorithm with Optimal Threshold

Content Provider	IEEE Xplore Digital Library
Author	Sung-Chiang Lin Wang, C. Zhen-Yu Wu Yu-Fang Chung
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Tunghai Univ., Taichung, Taiwan (Yu-Fang Chung) \|\| Inst. of Epidemiology & Preventive Med., Nat. Taiwan Univ., Taipei, Taiwan (Wang, C.) \|\| Dept. of Inf. Manage., Nat. Penghu Univ. of Sci. & Technol., Magong, Taiwan (Sung-Chiang Lin; Zhen-Yu Wu)
Abstract	Class imbalanced classifications are important issues in machine learning since class imbalanced problems usually happen in real applications such as intrusion detection, medical diagnostic/monitoring, oil-spill detection, and credit card fraud detection. It is hard to identify rare events correctly if a learning algorithm is just established based on optimal accuracy, as all samples will be classified into the major group. Many algorithms were proposed to deal with class imbalance problems. In this paper, we focus on MICE algorithm proposed by [15] and improve the algorithm by choosing the optimal threshold based on the posterior probabilities. In addition, we illustrate the reason why the logistic transformation works in MICE. The empirical results show that choosing the optimal threshold vis posterior probabilities can improve the performance of the MICE algorithm.
Starting Page	70
Ending Page	75
File Size	165621
Page Count	6
File Format	PDF
ISBN	9780769549743
DOI	10.1109/IMIS.2013.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Imbalanced data Sensitivity Accuracy Conferences Meta Imbalanced Classification Ensemble (MICE) Cost-sensitive learnin (CSL) Rare event detection Mice Partitioning algorithms Joints Logistics
Content Type	Text
Resource Type	Article

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