2014/06/18 by Jing Wang, Wang, Jing, Eunsik Park +4
Computer Science · Decision Sciences · Engineering · Mathematics · #Active learning (machine learning) #Advanced Statistical Process Monitoring #Artificial intelligence #Artificial neural network #Bayesian probability #Classifier (UML) #Computer science #Engineering #FOS: Computer and information sciences #Labeled data #Machine Learning and Algorithms #Machine Learning and Data Classification #Machine learning #Methodology (stat.ME) #Online machine learning #Process (computing) #Semi-supervised learning #Supervised learning #Task (project management) #stat.ME
paper · pdf · doi:10.48550/arxiv.1406.4676
25 pages, 8 figures
arxiv created 2014/06/18 · openalex publication_date 2014/06/18 · arxiv updated 2014/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Classification is an important task in many fields including biomedical research and machine learning. Traditionally, a classification rule is constructed based a bunch of labeled data. Recently, due to technological innovation and automatic data collection schemes, we easily encounter with data sets containing large amounts of unlabeled samples. Because to label each of them is usually costly and inefficient, how to utilize these unlabeled data in a classifier construction process becomes an important problem. In machine learning literature, active learning or semi-supervised learning are popular concepts discussed under this situation, where classification algorithms recruit new unlabeled subjects sequentially based on the information learned from previous stages of its learning process, and these new subjects are then labeled and included as new training samples. From a statistical aspect, these methods can be recognized as a hybrid of the sequential design and stochastic approximation procedure. In this paper, we study sequential learning procedures for building efficient and effective classifiers, where only the selected subjects are labeled and included in its learning stage. The proposed algorithm combines the ideas of Bayesian sequential optimal design and uncertainty sampling. Computational issues of the algorithm are discussed. Numerical results using both synthesized data and real examples are reported.