In daily life, people perform activities every moment differently from one another. Thus, it is necessary to develop a robust system that can recognize human activities and cope with their individual differences. In this article, we propose a new method of individualizing a classifier by choosing the most suitable one based on the estimation of compatibility with a set of classifiers, which we call compatibility-based classifier personalization (CbCP). To make CbCP effective and reduce the burden on the user, the number of activities that a user needs to perform to provide data should be as small as possible. We propose two methods of ranking activities that are as effective in estimating the compatibility as using all activities: difference-based and correlation-based approaches. Additionally, we evaluated four methods of handling a case when more than two classifiers have the same level of compatibility, i.e., multi-compatible classifier handling, random choice, average compatibility reference, and ensemble classification with and without weighting. An offline experiment was carried out using two public datasets, i.e., Physical Activity Monitoring for Aging People 2 (PAMAP2) and Daily Life Activities (DaLiAc), to understand the characteristics of these methods. The results showed that the correlation-based method for activity ranking and the average compatibility reference for multi-compatible classifier handling are the best combination in terms of classification performance, the burden on the user, and computational complexity.