Abstract-In general, the aim of our research is to adapt computational intelligence methods for computer-aided decision support in diagnosis and therapy of persons with Autism Spectrum Disorders (ASDs). In the paper, we are focusing on the data preprocessing step for cleaning a training data set for classifiers. An approach based on consistency factors is proposed.

Eye-tracking sequences can be considered in terms of complex networks. On the basis of complex network representation of eye-tracking data, we define a measure, derived from rough set theory, for assessing the cohesion of saccade connections between object components identified in visual stimuli used in eye-tracking experiments. Rough sets are an appropriate tool to deal with rough (ambiguous, imprecise) concepts. Theoretical foundations given in the paper are supplemented with a numerical example explaining the proposed approach.

In this paper, we deal with the problem of the initial analysis of data from evaluation sheets of subjects with autism spectrum disorders (ASDs). In the research, we use an original evaluation sheet including questions about competencies grouped into 17 spheres. An initial analysis is focused on the data preprocessing step including the filtration of cases based on consistency factors. This approach enables us to obtain simpler classifiers in terms of their size (a number of nodes and leaves in decision trees and a number of classification rules).

The main goal of our research is to build the ontology of places in Poland covering variety of aspects of places, mainly administrative and socio-economic. In the paper, we show a part of the ontology on the example of the Mazowieckie Voivodship. The ontology is being implemented using the OWL 2 Web Ontology Language.