A flexible procedure for mixture proportion estimation in positive‐unlabeled learning

摘要

Abstract > Positive‐unlabeled (PU) learning considers two samples, a positive set <fi>P</fi> with observations from only one class and an unlabeled set <fi>U</fi> with observations from two classes. The goal is to classify observations in <fi>U</fi> . Class mixture proportion estimation (MPE) in <fi>U</fi> is a key step in PU learning. Blanchard et al. showed that MPE in PU learning is a generalization of the problem of estimating the proportion of true null hypotheses in multiple testing problems. Motivated by this idea, we propose reducing the problem to one‐dimension via construction of a probabilistic classifier trained on the <fi>P</fi> and <fi>U</fi> data sets followed by application of a one‐dimensional mixture proportion method from the multiple testing literature to the observation class probabilities. The flexibility of this framework lies in the freedom to choose the classifier and the one‐dimensional MPE method. We prove consistency of two mixture proportion estimators using bounds from empirical process theory, develop tuning parameter free implementations, and demonstrate that they have competitive performance on simulated waveform data and a protein signaling problem. </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> > 积极的未标记（PU）学习考虑两个样本，一个积极的集合 <fi> p </ fi> 只有一个类和一个未标记的集合的观察 <fi> u </ fi> 两个课程的观察。目标是对观察分类 <fi> u </ fi> 。类混合比例估计（MPE） <fi> u </ fi> 是普学习的关键步骤。 Blanchard等人。显示PU学习中的MPE是估计多个测试问题中真假假设比例的问题的概括。受到这个想法的动机，我们建议通过训练概率的概率分类器来减少一个维度的问题 <fi> p </ fi> 和 <fi> u </ fi> 数据集，然后从多个测试文献中施加一维混合比例方法到观察类概率。该框架的灵活性在于选择分类器和一维MPE方法的自由度。我们证明了使用经验过程理论的边界的两个混合比例估计器的一致性，开发调整参数自由实施，并证明它们对模拟波形数据和蛋白质信号传导问题具有竞争性能。 </ p> </摘要> </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-28614/'>《Statistical Analysis and Data Mining》</a> <b style="margin: 0 2px;">|</b><span>2020年第2期</span><b style="margin: 0 2px;">|</b><span>共10页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> </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"> </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/3056.html" title="经济统计学">经济统计学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=classification&option=203" rel="nofollow">classification;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=empirical processes&option=203" rel="nofollow">empirical processes;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=local false discovery rate&option=203" rel="nofollow">local false discovery rate;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=mixture proportion estimation&option=203" rel="nofollow">mixture proportion estimation;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=multiple testing&option=203" rel="nofollow">multiple testing;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=PU learning&option=203" rel="nofollow">PU learning;</a> </p> <div class="translation"> 机译：分类;经验过程;局部假发现率;混合比例估计;多次测试;浦学习; </div> </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="/academic-journal-foreign_detail_thesis/0204121842362.html">A flexible procedure for mixture proportion estimation in positive‐unlabeled learning</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Zhenfeng Lin&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Zhenfeng Lin,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=James P. 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