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In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study. [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]

The present work describes a new model of pattern classification and its application to align instances from different ontologies, which are in turn related to e-learning educative content in a Knowledge Society context. In general, ontologies are the fundamental tool inherent to Semantic Web. In particular, the problem of ontology matching is modeled in this paper as a binary pattern classification problem. The original model presented here was validated through experiments, which were done on data taken from the OAEI (Ontology Alignment Evaluation Initiative) 2014 campaign, presented in the OWL (Web Ontology Language) format, as well as on data taken from two international repositories, ADRIADNE and MERLOT, in LOM (Learning Objects Metadata) format. The results obtained show a high precision measurement when compared against some of the best methods present in the state of the art. [ABSTRACT FROM AUTHOR]

As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia. [ABSTRACT FROM AUTHOR]

The Lernmatrix is a classic associative memory model. The Lernmatrix is capable of executing the pattern classification task, but its performance is not competitive when compared to state-of-the-art classifiers. The main contribution of this paper consists of the proposal of a simple mathematical transform, whose application eliminates the subtractive alterations between patterns. As a consequence, the Lernmatrix performance is significantly improved. To perform the experiments, we selected 20 datasets that are challenging for any classifier, as they exhibit class imbalance. The effectiveness of our proposal was compared against seven supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, logistic function, support vector machines, and neural networks). By choosing balanced accuracy as a performance measure, our proposal obtained the best results in 10 datasets. The elimination of subtractive alterations makes the new model competitive against the best classifiers, and sometimes beats them. After applying the Friedman test and the Holm post hoc test, we can conclude that within a 95% confidence, our proposal competes successfully with the most effective classifiers of the state of the art. [ABSTRACT FROM AUTHOR]