.png){kind=icon}
返回
A semi-supervised learning approach for model selection based on class-hypothesis testing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2017.08.006.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper deals with the topic of learning from unlabeled or noisy-labeled data in the context of a classification problem. In the classification problem the outcome yields one of a discrete set of values thus, assumptions on them could be established to obtain the most likely prediction model at the training stage. In this paper, a novel case-based model selection method is proposed, which combines hypothesis testing from a discrete set of expected outcomes and feature extraction within a cross-validated classification stage. This wrapper-type procedure acts on fully-observable variables under hypothesis-testing and improves the classification accuracy on the test set, or keeps its performance at least at the level of the statistical classifier. The model selection strategy in the cross validation loop allows building an ensemble classifier that could improve the performance of any expert and intelligence system, particularly on small sample-size datasets. Experiments were carried out on several databases yielding a clear improvement on the baseline, i.e., SPECT dataset Acc = 86.35 +/- 1.51, with Sen = 91.10 +/- 2.77, and Spe = 81.11 +/- 1.61. In addition, the CV error estimate for the classifier under our approach was found to be an almost unbiased estimate (as the baseline approach) of the true error that the classifier would incur on independent data. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Statistical learning and decision theory
Semi-supervised learning
Support vector machines (SVM)
Hypothesis testing
Partial least squares
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Case-Based Statistical Learning: A Non-Parametric Implementation With a Conditional-Error Rate SVM.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network利用大规模训练人工神经网络鉴别胸部低剂量CT良恶性结节的计算机辅助诊断方案
PREAI
分享收藏Computer-aided detection performance in mammographic examination of masses: AssessmentRADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏
查看更多内容