用遗传算法优化数据重要性重排，提升多种分类器性能。
A Novel Machine Learning Classifier Based on Genetic Algorithms and Data Importance Reformatting
- 基于数据重要性重排与遗传算法优化输入特征
- 在7个数据集上平均提升5.96%至16.79%准确率
- 尤其适合需要稳定提升性能的机器学习应用
本文提出一种名为GADIC的新分类算法，结合数据重要性（DI）重排与遗传算法（GA），以克服数据特性对机器学习分类器性能的制约。GADIC包含三个阶段：基于DI的数据重排、利用GA优化重排后训练数据的训练，以及测试阶段将相似训练实例均值化作为未知样本进行分类。该方法在支持向量机（SVM）、K近邻（KNN）、逻辑回归（LR）、决策树（DT）和朴素贝叶斯（NB）五种经典分类器上验证，使用了来自UCI ML库和Kaggle的七个公开数据集：克利夫兰心脏病、印度肝病患者、皮马印第安人糖尿病、员工未来预测、电信客户流失、银行客户流失及技术学生数据集。实验结果表明，除朴素贝叶斯在克利夫兰心脏病数据集上略有约1%准确率下降外，GADIC显著提升了多数分类器性能；其中KNN提升最大，其次为SVM，LR提升最小。平均提升幅度最低为5.96%，最高达16.79%。
原文摘要 · Abstract (English)
In this paper, a novel classification algorithm that is based on Data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%.
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