Computational Model for Artificial Learning Using Formal Concept Analysis

       The field of artificial intelligence embraces two approaches to artificial learning. The first is motivated by the study of mental processes and states that artificial learning is the study of mechanisms embodied in the human mind. It aims to understand how these mechanisms can be translated into computer programs. The second approach initiated from a practical computing standpoint and has less grandiose aims. It involves developing programs that learn from past data, and may be considered as a branch of data processing. In this paper, we are concerned with the first approach. Artificial learning is interested in the classification learning that is a learning algorithm for categorizing unseen examples into predefined classes based on a set of training examples. We formulated a computational model for binary classification process using formal concept analysis. The classification rules are derived and applied successfully for different study cases.