Lymph Diseases Prediction Using Random Forest and Particle Swarm Optimization

Waheeda Almayyan
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引用次数: 13

Abstract

This research aims to develop a model to enhance lymphatic diseases diagnosis by the use of random forest ensemble machine-learning method trained with a simple sampling scheme. This study has been carried out in two major phases: feature selection and classification. In the first stage, a number of discriminative features out of 18 were selected using PSO and several feature selection techniques to reduce the features dimension. In the second stage, we applied the random forest ensemble classification scheme to diagnose lymphatic diseases. While making experiments with the selected features, we used original and resampled distributions of the dataset to train random forest classifier. Experimental results demonstrate that the proposed method achieves a remark-able improvement in classification accuracy rate.
基于随机森林和粒子群优化的淋巴疾病预测
本研究旨在建立一个模型,利用随机森林集成机器学习方法训练一个简单的抽样方案,以提高淋巴疾病的诊断。本研究主要分为两个阶段:特征选择和分类。在第一阶段,利用粒子群算法和多种特征选择技术从18个特征中选择出一些判别特征来降低特征维数。在第二阶段,我们应用随机森林集合分类方案诊断淋巴疾病。在对选择的特征进行实验的同时,我们使用数据集的原始和重采样分布来训练随机森林分类器。实验结果表明,该方法在分类准确率上有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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