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Selection of An Initial Training Set for Active Learning Using Cluster-Based Sampling  

강재호 (부산대학교 컴퓨터공학과)
류광렬 (부산대학교 컴퓨터공학)
권혁철 (부산대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.7, 2004 , pp. 859-868 More about this Journal
Abstract
We propose a method of selecting initial training examples for active learning so that it can reach high accuracy faster with fewer further queries. Our method is based on the assumption that an active learner can reach higher performance when given an initial training set consisting of diverse and typical examples rather than similar and special ones. To obtain a good initial training set, we first cluster examples by using k-means clustering algorithm to find groups of similar examples. Then, a representative example, which is the closest example to the cluster's centroid, is selected from each cluster. After these representative examples are labeled by querying to the user for their categories, they can be used as initial training examples. We also suggest a method of using the centroids as initial training examples by labeling them with categories of corresponding representative examples. Experiments with various text data sets have shown that the active learner starting from the initial training set selected by our method reaches higher accuracy faster than that starting from randomly generated initial training set.
Keywords
Active teaming; Initial training set selection; flustering; Text classification;
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