Comparison of Prediction Methods for Air Pollution Data in Malaysia and Singapore

IF 1.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Merlinda Wibowo, Sarina Sulaiman, S. Shamsuddin
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引用次数: 6

Abstract

The process for analyzing and extracting useful information from a large database that employs one or more machine learning techniques is Data Mining. There are many data mining methods that can be used in a variety of data patterns. One of them is prediction modeling. This study compares several data mining performance methods for prediction such as Naïve Bayes, Random Tree, J48, and Rough Set to get the most powerful classifier to extract the knowledge of air pollution data. The parameters being used for observation in the performance of the prediction methods are correctly and incorrectly classified instances, the time taken, and kappa statistic. The experimental result reveals that Rough Set is extremely good for classifying the Air Pollutant Index (API) data from Malaysia and Singapore. Rough Set has the lowest error and the highest performance compared to other methods with the accuracy more than 97%.
马来西亚和新加坡空气污染数据预测方法的比较
从使用一种或多种机器学习技术的大型数据库中分析和提取有用信息的过程称为数据挖掘。有许多数据挖掘方法可用于各种数据模式。其中之一是预测建模。本研究比较了几种用于预测的数据挖掘性能方法,如Naïve贝叶斯、随机树、J48和粗糙集,以获得最强大的分类器来提取空气污染数据的知识。在预测方法的性能中用于观察的参数是正确和错误分类的实例、所花费的时间和kappa统计量。实验结果表明,粗糙集对马来西亚和新加坡的空气污染物指数(API)数据的分类效果非常好。与其他方法相比,粗糙集具有最低的误差和最高的性能,准确率在97%以上。
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来源期刊
CiteScore
3.20
自引率
20.00%
发文量
0
审稿时长
4.3 months
期刊介绍: The primary aim of the International Journal of Innovative Computing, Information and Control (IJICIC) is to publish high-quality papers of new developments and trends, novel techniques and approaches, innovative methodologies and technologies on the theory and applications of intelligent systems, information and control. The IJICIC is a peer-reviewed English language journal and is published bimonthly
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