Genetic Algorithm as Machine Learning for Profiles Recognition
Yann Carbonne, Christelle Jacob · 2015
Persons are often asked to provide information about themselves. These data are very heterogeneous and result in as many “profiles” as contexts. Sorting a large amount of profiles from different contexts and assigning them back to a specific individual is quite a difficult problem. Semantic processing and machine learning are key tools to achieve this goal. This paper describes a framework to address this issue by means of concepts and algorithms selected from different Artificial Intelligence fields. Indeed, a Vector Space Model is customized to first transpose semantic information into a mathematical model. Then, this model goes through a Genetic Algorithm (GA) which is used as a supervised learning algorithm for training a computer to determine how much two profiles are similar. Amongst the GAs, this study introduces a new reproduction method (Best Together), and compare it to some usual ones (Wheel, Binary Tournament). This paper also evaluates the accuracy of the GAs predictions for profiles clustering with the computation of a similarity score, as well as its ability to classify two profiles are similar or non-similar. We believe that the overall methodology can be used for any kind of sources using profiles and, more generally, for similar data recognition.