A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data
Pub. online: 2 July 2024
Type: Statistical Data Science
Open Access
Open Access
Received
21 January 2024
21 January 2024
Accepted
28 April 2024
28 April 2024
Published
2 July 2024
2 July 2024
Abstract
Classification is an important statistical tool that has increased its importance since the emergence of the data science revolution. However, a training data set that does not capture all underlying population subgroups (or clusters) will result in biased estimates or misclassification. In this paper, we introduce a statistical and computational solution to a possible bias in classification when implemented on estimated population clusters. An unseen-cluster problem denotes the case in which the training data does not contain all underlying clusters in the population. Such a scenario may occur due to various reasons, such as sampling errors, selection bias, or emerging and disappearing population clusters. Once an unseen-cluster problem occurs, a testing observation will be misclassified because a classification rule based on the sample cannot capture a cluster not observed in the training data (sample). To overcome such issues, we suggest a two-stage classification method to ameliorate the unseen-cluster problem in classification. We suggest a test to identify the unseen-cluster problem and demonstrate the performance of the two-stage tailored classifier using simulations and a public data example.
Supplementary material
Supplementary Material
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Supplementary document: The supplementary document provides the proofs of the Theorems 1, 2, and 3, and additional numerical study results.
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Software: R codes for the proposed methods and algorithms.
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