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Localized Generalization Error Model with Variable Size of Neighborhoods and Applications in Ensemble Feature Selection.

机译：邻域大小可变的局部化广义误差模型及其在集合特征选择中的应用

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摘要

The Localized Generalization Error Model (L-GEM) provides an upper bound of the generalization error for unseen samples located in the Q-neighborhood of each training sample. It originates from the idea that expecting a classifier to recognize unseen samples in the whole input space correctly is unreasonable, as some are very different from training samples.;A crucial parameter, Q, is used to adjust the size of neighborhood of each training sample. In current literature, a single Q value is selected and is used by all training samples; thus, all training samples will have the same size of neighborhood. However, certain training samples may be extremely close to one another, and the same size of neighborhood may result in large overlapping. One of the objectives is to study the selections of different Qs for individual training samples instead of selecting a single value for all.;In view of the high computational complexity of L-GEM with variable neighborhood sizes, the second objective is to propose a new point of view by clustering data into different groups instead of a single data point. The neighborhoods are considered for each cluster, instead of each training sample.;However the trend of performance of a classifier on different sizes of neighborhoods is ignored which provide important information to evaluate the classifier. Therefore the third objective of the thesis is to propose a set of L-GEM based indices to evaluate the classifier with different sizes of neighborhood. In addition, the performance of the proposed methods in different scenarios with outlier is provided.;L-GEM has been extended into a Multiple Classifier System (MCS), and it has been shown to evaluate the generalization capability of MCS successfully. To construct an MCS, creating diverse sets of classifiers is a key issue. The ensemble feature selection varies the feature sets for each individual classifier in an MCS. Promoting diversity alone may not generate MCS with high generalization capability. Therefore, a genetic algorithm (GA) and localized generalization error model for MCS (L-GEMMCS) will be adopted to select sets of diversified feature groups for constructing an MCS with high generalization capability.
机译：局部化广义误差模型（L-GEM）为位于每个训练样本Q邻域中的未见样本提供了泛化误差的上限。它源于这样一个想法，即期望分类器正确识别整个输入空间中未见到的样本是不合理的，因为有些样本与训练样本有很大不同。;关键参数Q用于调整每个训练样本的邻域大小。在目前的文献中，选择了一个Q值，所有训练样本都使用该值。因此，所有训练样本将具有相同的邻域大小。但是，某些训练样本可能彼此非常接近，并且相同大小的邻域可能会导致大量重叠。目标之一是研究针对单个训练样本的不同Q的选择，而不是针对所有样本选择单个值。鉴于具有可变邻域大小的L-GEM的高计算复杂性，第二个目标是提出一种新的方法。通过将数据聚类到不同的组而不是单个数据点来实现观点。为每个聚类而不是每个训练样本考虑邻域；然而，忽略了分类器在不同大小的邻域上的性能趋势，这为评估分类器提供了重要信息。因此，本文的第三个目标是提出一套基于L-GEM的指标来评估具有不同邻域大小的分类器。此外，还提供了所提出的方法在不同场景下具有离群值的性能。L-GEM已扩展到多分类器系统（MCS）中，并已证明可以成功评估MCS的泛化能力。要构建MCS，创建多样化的分类器集是关键问题。整体特征选择会更改MCS中每个单独分类器的特征集。仅促进多样性可能不会产生具有高泛化能力的MCS。因此，将采用遗传算法（GA）和MCS的局部化泛化误差模型（L-GEMMCS）来选择多样化特征组的集合，以构建具有高泛化能力的MCS。

著录项

  • 作者

    Chan, Po Fong.;

  • 作者单位

    Hong Kong Polytechnic University (Hong Kong).;

  • 授予单位 Hong Kong Polytechnic University (Hong Kong).;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 182 p.
  • 总页数 182
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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