In traditional machine learning, classification is typically undertaken in the way of discriminative learning by using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches can not achieve.