Fuzzy rule based systems for gender classification from blog data

Gender classification is a popular machine learning task, which has been involved in various application areas, such as business intelligence, access control and cyber security. In the context of information granulation, gender related information can be divided into three types, namely, biological information, vision based information and social network based information.
In traditional machine learning, gender identification has been typically treated as a discriminative classification task, i.e. it is aimed at learning a classifier that discriminates between male and female. In this paper, we argue that it is not always appropriate to identify gender in the way of discriminative classification,
especially when considering the case that both male and female people are of high diversity and thus individuals of different genders could have high similarity to each other in terms of their characteristics. In order to address the above issue, we propose the use of a fuzzy approach for generative classification of gender. In particular, we focus on gender classification based
on social network information. We conduct an experiment study by using a blog data set, and compare the fuzzy approach with C4.5, Naive Bayes and Support Vector Machine in terms of classification performance. The results show that the fuzzy approach outperforms the other approaches and is also capable of capturing the diversity of both male and female people and dealing with the fuzziness in terms of gender identification.