Imbalanced Data Set Modelling and Classification for Life Threatening/ Safety Critical Applications

针对危及生命/安全关键应用的不平衡数据集建模和分类

• 批准号: EP/G026858/1
• 负责人: Xia Hong
• 金额: $ 13万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FG026858%2F1
• 关键词: Imbalanced; Data; Set; Modelling; Classification

Machine learning from imbalanced data sets is related to a broad range of very important problems in many engineering and scientific disciplines, e.g. medical diagnostics, signal detection and machine/material fault detection. Apart from the highly practical value, data learning from imbalanced data sets is also of high theoretical interest. Because the performance metrics used in conventional classifier construction may break down when applied to the imbalanced data sets, this has motivated considerable researches in machine learning communities aimed at a variety of learning methodologies for the imbalanced data setsDespite significant research in machine learning for imbalanced data, there is still a need and/or a lack of general methodologies that are able to deliver the capability of knowledge discovery as demanded by many hugely important applications. For example, it is highly beneficial to discover new noninvasive biological markers from clinical data, which can improve early medical diagnostics results, in order to start early treatment of a cancer. The motivation of the proposed research can be illustrated by another example. In material science, suppose that new materials with exceptional properties, e.g. strength, are required for new mechanical structures, e. g. military vehicles. For this purpose, a sample of experimental trials is performed to obtain a new material together with the measurements of the properties. It is highly desirable that the properties/behaviours could be discovered, by resort of data modelling using a small sample, rather than performing many more unnecessary and very expensive engineering experiments (large sample).This proposal is concerned with the development of a new modelling approach which builds upon the state-of-the-art nonlinear modelling methodologies and is specifically designed for pattern recognition using the imbalanced data sets. The objectives of the research include the modelling, classification, class probability (risk) prediction and knowledge discovery from the imbalanced data sets which are commonly found in many associated applications.

在医学诊断、信号检测和机械/材料故障检测等许多工程和科学学科中，机器学习涉及到许多非常重要的问题。从不平衡数据集中进行数据学习除了具有很高的实用价值外，还具有很高的理论价值。由于传统分类器构造中使用的性能度量在应用于不平衡数据集时可能会崩溃，这促使了机器学习社区中针对不平衡数据集的各种学习方法的大量研究。尽管对不平衡数据的机器学习进行了大量研究，但仍然需要和/或缺乏能够提供许多非常重要的应用所要求的知识发现能力的通用方法。例如，从临床数据中发现新的非侵入性生物标志物，可以提高早期医学诊断结果，从而开始癌症的早期治疗，这是非常有益的。这项研究的动机可以用另一个例子来说明。在材料科学中，假设新的机械结构(如军用车辆)需要具有特殊性能的新材料，例如强度。为此，进行了一系列实验试验以获得一种新材料，并对其性能进行了测量。通过使用小样本的数据建模来发现属性/行为是非常理想的，而不是进行更多不必要和非常昂贵的工程实验(大样本)。这项建议涉及到一种新的建模方法的开发，该方法建立在最先进的非线性建模方法的基础上，并专门设计用于使用不平衡数据集进行模式识别。这项研究的目标包括建模、分类、类别概率(风险)预测和从不平衡数据集中发现知识，这些数据通常存在于许多相关应用中。

项目成果

An Elastic Net Orthogonal Forward Regression Algorithm

一种弹性网正交前向回归算法

• DOI: 10.3182/20120711-3-be-2027.00159
• 发表时间: 2012
• 期刊: IFAC Proceedings Volumes
• 作者: Hong X