On aggregation operators of transitive similarity and dissimilarity relations

Similarity and dissimilarity are widely used concepts. One of the most studied matters is their combination or aggregation. However, transitivity property is often ignored when aggregating despite being a highly important property, studied by many authors but from different points of view. We collect here some results in preserving transitivity when aggregating, intending to clarify the relationship between aggregation and transitivity and making it useful to design aggregation operators that keep transitivity property. Some examples of the utility of the results are also shown.Peer ReviewedPostprint (published version

Comparison of methods to predict ozone concentration

Several methods have been applied to the prediction of ozone concentration. In this work, an Heterogeneous Neural Network (HNN) is used to perform the same task. Different capabilities of HNN are exploited like imprecision in data or flexibility in the function computed by the neurons. The results obtained are compared with previous methodologies like Multi-layer Perceptron (MLP), Elman Network (EN), Modified Elman Network (MEN), Fuzzy Inductive Reasoning (FIR) and Long Short Term Memory Recurrent Neural Network (LSTM). Without being a Recurrent Network, HNN is able to get similar results than other methodologies, not far from them. More complex and specialized similarity functions can be developed in order to reach higher performances.Postprint (published version

Similarity and dissimilarity concepts in machine learning

Similarity and dissimilarity are rarely formalized concepts in Artificial Intelligence (AI). Similarity and dissimilarity have a psychological origin, and they have been adapted to AI. In this field, however, similarity and dissimilarity choice is not always dependent on the problem to solve. In this paper, a formalization of similarity and dissimilarity is presented. The purpose of this paper is to contribute to the design and understanding of similarity and dissimilarity in AI, increasing their general utility. A formal definition and some basic properties are introduced. Also, some transformation functions and similarity and dissimilarity operators are presented.Postprint (published version

Comparison of methods to predict ozone concentration

Several methods have been applied to the prediction of ozone concentration. In this work, an Heterogeneous Neural Network (HNN) is used to perform the same task. Different capabilities of HNN are exploited like imprecision in data or flexibility in the function computed by the neurons. The results obtained are compared with previous methodologies like Multi-layer Perceptron (MLP), Elman Network (EN), Modified Elman Network (MEN), Fuzzy Inductive Reasoning (FIR) and Long Short Term Memory Recurrent Neural Network (LSTM). Without being a Recurrent Network, HNN is able to get similar results than other methodologies, not far from them. More complex and specialized similarity functions can be developed in order to reach higher performances

Similarity and dissimilarity concepts in machine learning

Similarity and dissimilarity are rarely formalized concepts in Artificial Intelligence (AI). Similarity and dissimilarity have a psychological origin, and they have been adapted to AI. In this field, however, similarity and dissimilarity choice is not always dependent on the problem to solve. In this paper, a formalization of similarity and dissimilarity is presented. The purpose of this paper is to contribute to the design and understanding of similarity and dissimilarity in AI, increasing their general utility. A formal definition and some basic properties are introduced. Also, some transformation functions and similarity and dissimilarity operators are presented