Distance Metric Based Oversampling Method for Bioinformatics and Performance Evaluation.
An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority class but significantly less accurate for the m...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 7; pp. 1 - 10 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925380&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925380 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2016 vid: 40 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925380 115925380 115925380 10.1007/s10916-016-0516-3 115925380 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Distance Metric Based Oversampling Method for Bioinformatics and Performance Evaluation. aug: au: Tsai, Meng-Fong Yu, Shyr-Shen affil: Department of Computer Science and Engineering, National Chung Hsing University, Taichung 402 Taiwan sug: subj: Bioinformatics Algorithms Decision Support Techniques Human Descriptive Statistics Survival Analysis Breast Neoplasms Prognosis Female Female ab: An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority class but significantly less accurate for the minority class. To overcome this problem, this study took several imbalanced datasets from the famed UCI datasets and designed and implemented an efficient algorithm which couples Top-N Reverse k-Nearest Neighbor (TR kNN) with the Synthetic Minority Oversampling TEchnique (SMOTE). The proposed algorithm was investigated by applying it to classification methods such as logistic regression (LR), C4.5, Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN). This research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrate that the Euclidean and Manhattan distances are not only more accurate, but also show greater computational efficiency when compared to the Chebyshev and Cosine distances. Therefore, the proposed algorithm based on TR kNN and SMOTE can be widely used to handle imbalanced datasets. Our recommendations on choosing suitable distance metrics can also serve as a reference for future studies. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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