A strategy for dealing with missing values by using selective activation neurons in a multi-topology framework

Neural Networks (NN) have proven to be able to successfully solve problems in many areas. However, for large scale real problems, data is often incomplete. This is a serious problem, because NN cannot handle directly missing values. The usual approach to solve this problem consists of removing attributes and/or samples containing unknown values. This strategy is very attractive since it is simple to implement and reduces the dimensionality of data, therefore potentially reducing the complexity of the problem. However removing features or instances containing vital information, which can not be compensated by the remaining data, may result in the unattainability of accurate NN models. Another strategy consists of estimating missing values. However, wrong estimations of crucial data can lead to unpredicted results. Moreover these techniques do not account for real situations (e.g. where sensors may fail) and the output of such NN models may cause instabilities in the whole process. In this paper we propose a technique for handling missing values that accounts for the creation of different transparent NN models with respect to the missing features instead of relying on tedious data pre-processing techniques. The resulting models are bounded to share information among them. Contrary to the imputation of data (estimate) our models take into account the uncertainty caused by unknown values. Moreover the presented technique is prepared to deal with faulty sensors. The preliminary results obtained in several datasets show the efficacy of the proposed approach.