This paper explores a novel Learning Classifier System (LCS) that can detect shoplifting behavior from the class-imbalanced sequence data of customer-behaviors. The shoplifting behavior detection is related to the sequence labeling as a time-series classification. More importantly, the target problem has a difficulty of the class-imbalanced problem in addition to the time-series classification because the number of data of shoplifters might be much fewer than that of customers who do not shoplift. To tackle this difficult issue, this paper proposes a feature extraction method for the sequence labeling in the class-imbalanced problem and applies it into XCS-SL (LCS for sequence labeling). The proposed LCS (called as XCS-SLFEM (XCS-SL for Feature Extraction of Minority class data)) extracts the features of the minority class from those of the majority one by representing them as the classifiers. The intensive simulation using the customer-behavior dataset that includes the shoplifting behaviors has revealed the following implications: (1) XCS-SLFEM shows the superior performance as compared with XCS-SL in the customer-behavior dataset including the class-imbalanced sequence data; (2) the classification accuracy of XCS-SLFEM increases as the size of memory for the sequence histories increases; and (3) XCS-SLFEM has the potential of predicting shoplifting before the shoplifters do shoplift.