用于分类虚假房地产列表的集群分析
Maifuza Mohd Amin1, Nor Samsiah Sani1, Mohammad Faidzul Nasrudin1
1Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
PeerJ. Computer science
|July 10, 2024
概括
集群分析有效地识别出虚假的房地产列表. 这种方法显著提高了分类准确性,创造了平衡的数据集,以改善在线房地产市场的欺诈检测.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 房地产分析 房地产分析
背景情况:
- 在线房地产平台的扩散导致欺诈性上市的增加,造成了用户的重大损害.
- 现有的房地产欺诈检测方法有限,特别是在租和销售平台上区分假名单.
研究的目的:
- 应用集群分析来将房地产上市列表分类为真实或假的.
- 为了提高假名单检测的准确性,使用优化的K-means集群.
主要方法:
- 在专家策划的数据集上进行数据预处理和功能工程.
- 使用Silhouette系数,Calinski-Harabasz指数和Davies-Bouldin指数进行参数优化的K-means集群算法.
- 使用随机森林和决策树分类器对集群性能的评估,Camberra距离度量被认为是最佳的.
主要成果:
- 优化K-means集群成功地将假冒物业与真正的房地产清单区分开来.
- 集群方法使随机森林分类模型的准确性提高了96%.
- 该研究产生了平衡的数据集,包括不同的虚假和真实的房地产列表集群.
结论:
- 集群分析,特别优化的K-means,为识别虚假房地产上市提供了强大的解决方案.
- 这种方法提高了在线物业平台的可靠性,并有助于未来深度学习模型的开发.
- 这些发现有助于创造一个更安全,更可信的房地产交易环境.
关键词:
卡林斯基 - 哈拉巴兹指数卡姆贝拉是一个卡姆贝拉.集群分析分析集群分析戴维斯 - 博尔丁指数虚假的房地产上市信息是假的雅卡德距离是雅卡德距离.K-意味着K的意思是K.随机的森林随机的森林房地产 不动产 不动产轮系数 是一个轮系数.更多相关视频
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