Data Mining in Incomplete Numerical and Categorical Data Sets: A Neuro Fuzzy Approach

- There are many applications dealing with incomplete data sets that take different approaches to making imputations for missing values. Most tackle the problem for numerical input variables in the data set. However, when there are two types of input variables, numerical and categorical, the state of the art has provided no clear solutions. This paper presents a proposal for handling incomplete numerical and categorical data sets using an extension of an existing neuro-fuzzy approach. The method is extensively tested in a real environment in the field of the political election polls.

• Type: Journal
• Year: 2009
• Where: ICAI
• Authors: Pilar Rey del Castillo, Jesus Cardenosa
• Keywords: Artificial Intelligence, ICAI 2009, Input Variables, Numerical, Numerical Input Variables