PMID- 35327833
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220329
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 24
IP  - 3
DP  - 2022 Feb 23
TI  - A Boundary-Information-Based Oversampling Approach to Improve Learning 
      Performance for Imbalanced Datasets.
LID - 10.3390/e24030322 [doi]
LID - 322
AB  - Oversampling is the most popular data preprocessing technique. It makes 
      traditional classifiers available for learning from imbalanced data. Through an 
      overall review of oversampling techniques (oversamplers), we find that some of 
      them can be regarded as danger-information-based oversamplers (DIBOs) that create 
      samples near danger areas to make it possible for these positive examples to be 
      correctly classified, and others are safe-information-based oversamplers (SIBOs) 
      that create samples near safe areas to increase the correct rate of predicted 
      positive values. However, DIBOs cause misclassification of too many negative 
      examples in the overlapped areas, and SIBOs cause incorrect classification of too 
      many borderline positive examples. Based on their advantages and disadvantages, a 
      boundary-information-based oversampler (BIBO) is proposed. First, a concept of 
      boundary information that considers safe information and dangerous information at 
      the same time is proposed that makes created samples near decision boundaries. 
      The experimental results show that DIBOs and BIBO perform better than SIBOs on 
      the basic metrics of recall and negative class precision; SIBOs and BIBO perform 
      better than DIBOs on the basic metrics for specificity and positive class 
      precision, and BIBO is better than both of DIBOs and SIBOs in terms of integrated 
      metrics.
FAU - Li, Der-Chiang
AU  - Li DC
AD  - Department of Industrial and Information Management, National Cheng Kung 
      University, University Road, Tainan 70101, Taiwan.
FAU - Shi, Qi-Shi
AU  - Shi QS
AD  - Department of Industrial and Information Management, National Cheng Kung 
      University, University Road, Tainan 70101, Taiwan.
FAU - Lin, Yao-San
AU  - Lin YS
AUID- ORCID: 0000-0002-5922-5971
AD  - Singapore Centre for Chinese Language, Nanyang Technological University, Ghim Moh 
      Road, Singapore 279623, Singapore.
FAU - Lin, Liang-Sian
AU  - Lin LS
AD  - Department of Information Management, National Taipei University of Nursing and 
      Health Sciences, Ming-te Road, Taipei 112303, Taiwan.
LA  - eng
PT  - Journal Article
DEP - 20220223
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC8947752
OTO - NOTNLM
OT  - boundary information
OT  - imbalanced datasets
OT  - synthetic sample generation
COIS- The authors declared that there is no conflict of interest.
EDAT- 2022/03/26 06:00
MHDA- 2022/03/26 06:01
PMCR- 2022/02/23
CRDT- 2022/03/25 01:05
PHST- 2022/01/04 00:00 [received]
PHST- 2022/02/19 00:00 [revised]
PHST- 2022/02/21 00:00 [accepted]
PHST- 2022/03/25 01:05 [entrez]
PHST- 2022/03/26 06:00 [pubmed]
PHST- 2022/03/26 06:01 [medline]
PHST- 2022/02/23 00:00 [pmc-release]
AID - e24030322 [pii]
AID - entropy-24-00322 [pii]
AID - 10.3390/e24030322 [doi]
PST - epublish
SO  - Entropy (Basel). 2022 Feb 23;24(3):322. doi: 10.3390/e24030322.