Brazil

As technology evolves and electronic devices become widespread, the amount of data produced in the form of stream increases in enormous proportions. Data streams are an online source of data, meaning that it keeps producing data continuously. This creates the need for fast and reliable methods to analyse and extract information from these sources. Stream mining algorithms exist for this purpose, but the use of supervised machine learning is extremely limited in the stream domain since it is unfeasible to label every data instance requested to be processed. Tackling this problem, our paper proposes the use of active learning techniques for stream mining algorithms, specifically incremental Hoeffding trees-based. It is important to mention that the active learning techniques were implemented to match the stream mining constraints regarding low computational cost. We took advantage of the incremental tree original structure to avoid overburdening the original computational cost when selecting a label. In other words, the statistical strategy to grow each incremental tree has supported the execution of active learning. Using techniques of uncertainty sampling, we were able to drastically reduce the number of labels required at the cost of a very small reduction in accuracy. Particularly with Budget Entropy there was an average negative impact of accuracy about 4% using only 14% of samples labelled.

BRACIS 2020

Active Learning embedded in incremental decision trees

The 27th International Conference on Systems, Signals and Image Processing.

IWSSIP 2020

technical paper

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Visualizing multidimensional Big Data is defying: high dimensionalities hinder or even preclude visual inspections. A means of tackling this issue is to use DR (Dimensionality Reduction) techniques, producing low-dimensional representations of high-dimensional data. Popular DR algorithms (e.g., Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding), albeit helpful, are computationally expensive. Most have quadratic or cubic ATC (Asymptotic Time Complexity) and/or calculate pairwise distances of the entire data set, exceeding available memory and rendering Big Data DR time-consuming or impracticable. These issues impede the employment of DR for online learning appliances, where recurrent, cumulative model updates are habitual. The stochastic factor of some approaches similarly obstructs any meaningful inspection on how knowledge is spatially disposed. The recently introduced PCS (Polygonal Coordinate System)—an incremental, geometric-based technique with linear ATC—is compelling; however, its restriction to 2-D embeddings amounts to significant information loss. We propose the Big Data ready, incremental PES (Pyramidal Embedding System), which builds on PCS virtues by additionally generating 3-D embeddings through its pyramid-like interspace, mitigating quality degradation. Visual inspections, as well as pairwise distance based statistical analyses, validate the PES ability to retain structural arrangements when embedding high- and low-dimensional data while retaining flexibility in resources consumption.

An Online Pyramidal Embedding Technique for High Dimensional Big Data Visualization

As commodities milho e soja são produtos consumidos em grande escala no mundo. As flutuações nos preços de mercado têm efeitos de longo alcance sobre os consumidores, agricultores e processadores de grãos. Assim, a previsão dos preços desses grãos tem atraído significativa atenção dos pesquisadores. Os modelos de previsão geralmente usam dados quantitativos de série temporal. No entanto, fatores qualitativos externos podem influenciar os dados em séries temporais, como eventos políticos, crises econômicas e o mercado de câmbio estrangeiro. Essas informações não são explícitas nos dados da série temporal e esses fatores podem influenciar a previsão dos valores das variáveis. Os dados textuais extraídos de notícias, fóruns e redes sociais podem ser uma fonte de conhecimento sobre fatores externos e potencialmente úteis para modelos de previsão de séries temporais. Alguns estudos apresentam técnicas de mineração de texto para combinar dados textuais com séries temporais. Porém, as representações existentes apresentam algumas limitações, como a maldição da dimensionalidade e atributos ineficazes. Este trabalho aplica métodos de pré-processamento em séries temporais e usa representações combinadas com dados textuais para prever o preço futuro do milho e da soja. Os resultados indicam que os métodos utilizados podem ser uma alternativa para melhorar o desempenho de previsão em tarefas de regressão.

Previsão do preço futuro do milho e da soja: Uma análise do uso de informação textual para enriquecer séries temporais.

Automatic diagnoses of diseases has been a long term challenge for Computer Science and related disciplines. Textual clinical reports can be used as a great source of data for such diagnoses. However, building classification models from them is not a trivial task. The problem tackled in this work is the identification of the medical diagnoses that are indicated in these reports. In the past, several methods have been proposed for addressing this problem, but a method developed for reports in the cardiology area that are written in Portuguese is still needed. In this paper we describe a method that is able to handle the peculiarities of clinical reports, including the medical terminology, and that is implemented to estimate correctly the disease based on raw clinical reports and a list of the possible diagnoses. Experimental results show that our method has a high degree of accuracy, even for infrequent classes and complex databases.

Automated classification of cardiology diagnoses based on textual medical reports

Labor Accidents in Brazil: a Descriptive Analysis

The opinion issued by consumers of products and services has become increasingly valued, both by other consumers and by companies. The automatic interpretation of review texts to generate information is of paramount importance. With opinion mining at the aspect level, it is possible to extract and summarize opinions about different components of a product or service. This paper evaluates the behavior of a method for extracting aspects using natural language processing tools for the Portuguese language. The aim is to investigate the maturity of the tools for Portuguese compared to the already consolidated tools for the English language. The evaluation was carried out in three datasets from two different domains with original texts in Portuguese and their translations into English, and vice versa, and the results indicate that there is no difference between languages.

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Next from BRACIS 2020

An Online Pyramidal Embedding Technique for High Dimensional Big Data Visualization

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Energy Consumption Prediction using Data Stream Learning for Commercial Buildings

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