An effective ensemble classification framework using random forests and a correlation based feature selection technique

Chutia Dibyajyoti; Bhattacharyya Dhruba Kumar; Sarma Jaganath; Raju Penumetcha Narasa Lakshmi

摘要

Abstract >Accurate classification of heterogeneous land surfaces with homogeneous land cover classes is a challenging task as satellite images are characterized by a large number of features in the spectral and spatial domains. The identifying relevance of a feature or feature set is an important task for designing an effective classification scheme. Here, an ensemble of random forests (RF) classifiers is realized on the basis of relevance of features. Correlation‐based Feature Selection (CFS) was utilized to assess the relevance of a subset of features by studying the individual predictive ability of each feature along with the degree of redundancy between them. Predictability of RF was greatly improved by random selection of the relevant features in each of the splits. An investigation was carried out on different types of images from the Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and QuickBird sensors. It has been observed that the performance of the RF classifier was significantly improved while using the optimal set of relevant features compared with a few of the most advanced supervised classifiers such as maximum likelihood classifier (MLC), Navie Bayes, multi‐layer perception (MLP), support vector machine (SVM) and bagging. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>抽象</ title> >与均匀陆地覆盖类的异构陆地表面的准确分类是一个具有挑战性的任务，因为卫星图像是一个具有挑战性的任务以光谱和空间域中的大量特征为特征。特征或特征集的识别相关性是设计有效分类方案的重要任务。这里，基于特征的相关性实现随机森林（RF）分类器的集合。基于相关的特征选择（CFS）来利用通过研究每个特征的个体预测能力以及它们之间的冗余程度来评估特征子集的相关性。通过随机选择每个分裂中的相关特征，大大改善了RF的可预测性。从Landsat增强专题映射器（Landsat ETM +）和Quickbird传感器的不同类型图像上进行了调查。已经观察到，与最高似然分类器（MLC），Navie Bay，多层感知（MLP）相比，使用最佳的相关分类器（如最高似然分类器（MLC），多层感知（MLP）相比，使用最佳的相关分类器（MLP ），支持向量机（SVM）和袋装。</ p> </ abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" > 著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-31674/'>《Transactions in GIS: TG 》</a> <b style="margin: 0 2px;">|</b><span>2017年第6期</span><b style="margin: 0 2px;">|</b> <span>共14页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Chutia Dibyajyoti&option=202" target="_blank" rel="nofollow">Chutia Dibyajyoti;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Bhattacharyya Dhruba Kumar&option=202" target="_blank" rel="nofollow">Bhattacharyya Dhruba Kumar;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Sarma Jaganath&option=202" target="_blank" rel="nofollow">Sarma Jaganath;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Raju Penumetcha Narasa Lakshmi&option=202" target="_blank" rel="nofollow">Raju Penumetcha Narasa Lakshmi;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>North Eastern Space Applications Centre Department of SpaceShillong 793103 India;</p> <p>Department of Computer Science and EngineeringTezpur UniversityNapaam 784028 Assam India;</p> <p>Department of Information TechnologySikkim Manipal Institute of TechnologyMaghitar Sikkim 737136 India;</p> <p>North Eastern Space Applications Centre Department of SpaceShillong 793103 India;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li> <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span> <a href="https://www.zhangqiaokeyan.com/clc/4940.html" title="测绘数据库与信息系统">测绘数据库与信息系统 ;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> </p> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704024965771.html">An effective ensemble classification framework using random forests 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