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Analysis and prediction of children's behavior in kindergarten is a current need of the Cuban educational system. Despite such an early age, the kindergarten institutions are devoted to facilitate the integral children development. However, the early detection of high capabilities in a child is not always accomplished accurately; due to teachers being mostly focused on the performance of the children that are lagging behind to achieve their age range's stated goals. In addition, the amount of children with high capabilities is usually low, which makes the prediction an imbalanced data problem. Thus, such children tend to be misguided and overlaid, with a negative impact in their sociological development. The purpose of this research is to propose an efficient algorithm that enhances the prediction in the kindergarten children data. We obtain a useful set of instances and features, thus improving the Nearest Neighbor accuracy according to the Area under the Receiving Operating Characteristic curve measure. The obtained results are of great interest for Cuban educational system, regarding the rapidly and precise prediction of the presence or absence of high capabilities for integral personality development in kindergarten children. [ABSTRACT FROM AUTHOR]

In this paper the Naïve Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed. [ABSTRACT FROM AUTHOR]