Comparative Study of Classification Algorithms for Various DNA Microarray Data
- Authors
- Kim, Jingeun; Yoon, Yourim; Park, Hye-Jin; Kim, Yong-Hyuk
- Issue Date
- Mar-2022
- Publisher
- MDPI
- Keywords
- classification; microarray; machine learning; multilayer perceptron; random forest; decision tree; support vector machine; k-nearest neighbors
- Citation
- GENES, v.13, no.3
- Journal Title
- GENES
- Volume
- 13
- Number
- 3
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84051
- DOI
- 10.3390/genes13030494
- ISSN
- 2073-4425
- Abstract
- Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and k-Nearest Neighbors (KNN), and the resulting accuracies were compared. k-fold cross-validation was used in evaluating the performance and the result was analyzed by comparing the performances of the five machine learning methods. Through the experiments, it was observed that the two tree-based methods, DT and RF, showed similar trends in results and the remaining three methods, MLP, SVM, and DT, showed similar trends. DT and RF generally showed worse performance than other methods except for one dataset. This suggests that, for the effective classification of microarray data, selecting a classification algorithm that is suitable for data traits is crucial to ensure optimum performance.
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Collections - 바이오나노대학 > 식품생물공학과 > 1. Journal Articles
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