Tri-training: Exploiting unlabeled data using three classifiers

doi:10.1109/TKDE.2005.186

Return

Tri-training: Exploiting unlabeled data using three classifiers

2005-11-01

1.0K

PRE

Zhi‐Hua Zhou

李鸣 (Ming Li)

DOI：10.1109/TKDE.2005.186

Original

Original request for help

Save

Abstract

En 中文

In many practical data mining applications, such as Web page classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have attracted much attention. In this paper, a new co-training style semi-supervised learning algorithm, named tri-training, is proposed. This algorithm generates three classifiers from the original labeled example set. These classifiers are then refined using unlabeled examples in the tri-training process. In detail, in each round of tri-training, an unlabeled example is labeled for a classifier if the other two classifiers agree on the labeling, under certain conditions. Since tri-training neither requires the instance space to be described with sufficient and redundant views nor does it put any constraints on the supervised learning algorithm, its applicability is broader than that of previous co-training style algorithms. Experiments on UCl data sets and application to the Web page classification task indicate that tri-training can effectively exploit unlabeled data to enhance the learning performance.

Keywords:

data mining

machine learning

learning from unlabeled data

semi-supervised learning

co-training

tri-training

Web page classification

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.