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The usual practice in the classification problem is to create a set of labeled data for training and then use it to tune a classifier for predicting the classes of the remaining items in the dataset. However, labeled data demand great human effort, and classification by specialists is normally expensive and consumes a large amount of time. In this paper, we discuss how we can benefit from a cluster-based tree kNN structure to quickly build a training dataset from scratch. We evaluated the proposed method on some classification datasets, and the results are promising because we reduced the amount of labeling work by the specialists to 4% of the number of documents in the evaluated datasets. Furthermore, we achieved an average accuracy of 72.19% on tested datasets, versus 77.12% when using 90% of the dataset for training.

Using the Cluster-based Tree Structure of k-Nearest Neighbor to Reduce the Effort Required to Classify Unlabeled Large Datasets