基于强化学习的分类算法正确选择用于非酒精性脂肪肝预测

中文摘要 AI 辅助

人工智能领域存在许多复杂问题。其中一些问题通过其他人工智能方法来解决，这被称为“人工智能为人工智能”。寻找合适的分类器算法是一项耗时的任务。因此，一种能够自动学习分类算法选择的算法非常重要。分类算法在预测各种疾病方面很有用。此外，原发性胆汁性肝硬化是最著名的可通过分类算法预测的疾病之一。本研究最重要的成就和新颖之处在于通过一种称为平方学习（SL）的强化学习评分方法实现学习的自动增加。在本研究中，提出了一种算法，该算法学习自动选择适当的分类算法来预测原发性胆汁性肝硬化。在本文中，受分类算法中四种评估指标的启发，提出了一种名为“四次学习”的新型强化学习方法。在本研究中，我们将此方法中使用的分类算法的性能从63%的准确率提高到98%的准确率。

英文摘要

There are many complex issues in the world of artificial intelligence. Some of these problems are solved using other artificial intelligence methods, which are called artificial intelligence for artificial intelligence. Finding an appropriate classifier algorithm is a time-consuming task. For this reason, an algorithm that can automatically learn the choice of classification algorithms is very important. Classification algorithms are useful in predicting various diseases. Also, Primary Biliary Cirrhosis is one of the most well-known diseases that have been predicted by classification algorithms. This research's most significant achievement and novelty is the automatic increase in learning through a scoring method of reinforcement learning is called square learning (SL). In this research, an algorithm is presented that learns to automatically select the appropriate classification algorithm to predict Primary Biliary Cirrhosis. In this article, with inspiration from four evaluation metrics in classification algorithms, a new reinforcement learning method by the name of Fourth Degree Learning has been presented. In this research, we increased the performance of the classification algorithms used in this method from 63% of accuracy and achieved 98% accuracy.

发表机构

• Islamic Azad University（伊斯兰阿扎德大学）

机构由 AI 辅助整理，请以论文原文为准。