极限学习机回归与分类算法及其在矿产预测中的应用
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- 论文作者：陈永良 ; 伍伟
- 年：2016
- 作者机构：吉林大学综合信息矿产预测研究所;长春市城乡规划设计研究院;
- 论文关键词：极限学习机 ; 矿产靶区预测 ; ROC曲线 ; ROC曲线下方面积(AUC) ; 约登指数
- 会议召开时间：2016-10-21
- 会议录名称：第十五届全国数学地质与地学信息学术研讨会论文集
- 英文会议录名称：Proceedings of the 15th National Workshop on Mathematical Geosciences and Geoinformatics
- 语种：中文
- 分类号：P612
- 学会代码：ZSYD
- 会议名称：第十五届全国数学地质与地学信息学术研讨会
- 会议地点：中国湖南长沙
- 主办单位：中国地质学会数学地质与地学信息专业委员会、中南大学、湖南省国土资源厅、湖南省地质学会
- 学会名称：中国地质学会数学地质与地学信息专业委员会
- 页数：16
- 文件大小：1672k
- 原文格式：D
- 会议级别：全国
摘要
人工神经网络方法是矿产资源评价领域应用广泛的一类非线性统计方法。但是,常用的神经网络方法需要预先定义一系列初始化参数而且模型训练过程收敛速度缓慢、容易出现过拟合现象。极限学习机是一种新的单一层前馈神经网络模型学习算法,该算法的初始化参数少、学习训练速度快而且模型泛化性能强。作为一种新的非线性回归与分类模型,极限学习机已广泛应用于机器学习领域。鉴于此,我们选择青海省拉陵灶火地区为实验研究区,将研究区划分成37,400个网格统计单元(含矿单元17个),构建了基于极限学习机回归和分类的多金属矿产靶区预测非线性统计模型,预测了研究区的多金属矿产靶区,用ROC(Receiver Operating Characteristic Curve)曲线评价了模型的矿产靶区预测效果,用AUC(ROC曲线下方面积)评价了模型的矿产靶区预测总体效果,用约登指数确定了矿产靶区与非靶区的最佳分界线。研究结果表明:1 ROC曲线特征和AUC统计值揭示极限学习机模型能够很好地区分研究区含矿和非含矿统计单元;2应用约登指数圈定的最优预测靶区占研究区总面积比例小(2.66~3.66%)但最优靶区包含了研究区绝大多数的已知多金属矿床(点)(82%);3极限学习机回归和分类模型的学习训练过程耗时短(75.6~518.3秒)。由此可见,极限学习机可以作为一种性能优越的数据驱动型矿产预测模型。
引文
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