特征选择
计算机科学
特征(语言学)
支持向量机
遗传算法
数据挖掘
人工智能
机器学习
选择(遗传算法)
模式识别(心理学)
语言学
哲学
作者
Asil Oztekin,Lina Al-Ebbini,Zülal Şevkli,Dursun Delen
标识
DOI:10.1016/j.ejor.2017.09.034
摘要
Feature selection, a critical pre-processing step for data mining, is aimed at determining representative variables/predictors from a large and feature-rich dataset for development of an effective prediction model. The purpose of this paper is to develop a hybrid methodology for feature selection using genetic algorithms to identify such representative features (input variables) and thereby to ensure the development of the best possible analytic model to predict and explain the target variable, quality of life (QoL), for patients undergoing a lung transplant overseen by the United Network for Organ Sharing (UNOS). The evaluation of three classification models, GA-kNN, GA-SVM, and GA-ANN, demonstrated that performance of the lung transplantation process has significantly improved via the GA-SVM approach, although the other two models have also yielded considerably high prediction accuracies. This study is unique in that it proposes a hybrid GA-based feature selection methodology along with design and development of several highly accurate classification algorithms to identify the most important features in the large and feature rich UNOS transplant dataset for lung transplantation.
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