Brazil

The DBSCAN algorithm is a traditional density-based clustering
method. This algorithm allows to identify groups of different topological for-
mats and also to discard isolated noises. The DBSCAN usually presents good
results, however, it performs several calculations of distances in the clustering
process, causing low efficiency, and its application in large datasets is not re-
commended. This work presents a new sampling method that makes it possible
to apply DBSCAN to a reduced set of examples, in order to approximate the ori-
ginal result of the algorithm applied to the entire dataset. Therefore, the method
makes it possible to execute large datasets with results similar to DBSCAN on
entire dataset, however, with greater computational efficiency.

BRACIS 2020

A biased sampling method for applying DBSCAN

machine learning

clustering

unsupervised learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

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.

We describe an intelligent triage system for emergency rooms; the system interacts with patients and classifies them by priority level and with respect to medical specialty. The system consists of a conversational interface, coupled with sensors and a physical robot-like platform, and classifiers that operate on symptoms and measurements so as to select a medical specialty and to output a priority level. Tests with human subjects demonstrated that our Healthbot system was well received and in fact preferred to alternatives. Tests have also shown that the classifiers reached accuracy consistent with a doctor’s output.

AUTOMATED EMERGENCY ROOM TRIAGE: HELPING PATIENTS GET THE BEST TREATMENT

Sample bias is a common issue on traditional machine learning studies but rarely considered when discussing meta-learning. It happens when the
training data sample lacks or overemphasizes one or more characteristics, compared to others. Herewith, models trained on such data may become inaccurate
for some instances. This work aims to analyze this issue in the meta-learning
context. Indeed, in most of the meta-learning literature, a random sample of
datasets is taken for building meta-models. Nonetheless, there is no discussion over a possible side-effect bias not controlled in such random sampling.
This work aims to analyze these effects, in order not only to discuss their consequences, but also to start a debate over the need of their consideration in
meta-learning research.

Sample Bias Effect on Meta-learning

The huge amount of textual data made available every day on the internet encouraged research in several areas that automatically process and analyze these texts. One of the most popular areas is Sentiment Analysis (SA), but although SA has been a topic widely discussed in recent years, it still faces a shortage of available resources in the Brazilian Portuguese language. This work presents the steps for a complete process of sentiment analysis and classification, from the development of a dataset in Brazilian Portuguese (in the books domain) to the sentiment classification, using some of the main classifiers in the literature and different feature extraction methods.

A Sentiment Classification Approach for Books Reviews in Brazilian Portuguese Using Different Feature Extraction Methods

In this work, a tool for inferring the degree of difficulty of cooking recipes will be proposed. The inference will be made by the textual classification of the recipe preparation methods. The tool will be a fundamental piece to the development of a context-aware cooking recipe recommendation system. Some of the main classifiers in Text Classification literature will be adopted, in addition to different feature extraction methods. An experimental evaluation is performed, in order to select the best approaches to compose the system.

Automatic Cooking Recipe Difficulty Level Inference using Natural Language Processing Techniques

Presentation of the article A glance of gastronomic tourism: A case on TripAdvisor, in which the steps and methods used to extract knowledge from online reviews of Brazilian restaurants will be explained.

A glance of gastronomic tourism: A case on TripAdvisor

The correct identification of plants species is an important task in several areas. The identification is even more important when the consumption and use of those plants are adopted as a way of preventing and treating diseases, where incorrect classification can bring serious consequences. In this work, an automatic plant species identification system is proposed, using machine learning and computer vision techniques. For the experimental evaluation, a real database of medicinal plants, composed of color, shape and texture features from the leaves, is proposed. Some of the main classifiers in the Machine Learning literature were adopted and evaluated.

Medicinal Plant Recognition Using Color, Texture and Shape Features

The quota traveling car renter problem (QCaRS) consists of minimizing the cost of a trip between a group of cities, visiting only a subgroup, and ensuring the visit of cities that meet with minimum satisfaction for the traveler. Evolutionary algorithms are methods often used to solve the QCaRS problem. Reproducing the results obtained by existing algorithms in the literature can be difficult if the authors did not make their implementations available. This paper presents a C++ library for the development of evolutionary algorithms applied to QCaRS. The library uses the Strategy design pattern to provide the exchange of components of evolutionary algorithms and thus facilitate the evaluation of new solution proposals for the QCaRS problem.

A C++ Library for Developing Evolutionary Algorithms to the QCaRS Problem

Floresta Aleatória é um algoritmo popular e efetivo na resolução de problemas de classificação e regressão. As predições de uma Floresta Aleatória são feitas considerando que cada árvore possui igual contribuição no resultado final. Este trabalho propõe um novo método de ponderação de árvores de regressão com o objetivo de melhorar o poder de predição do modelo. Nossa estratégia é motivada em utilizar medidas de dispersão estatística, como desvio padrão ou erro padrão da média, como indicadores de qualidade da predição na folha. A estratégia de ponderação proposta foi comparada com outros métodos de ponderação. Nessa comparação observou-se que a mesma reduziu o Erro Absoluto Médio em cerca de 30% dos conjuntos de dados estudados.

Optimizing Random Forest from the pondering of regression tree leaves

We introduce robot journalists that cover two pressing topics in
Brazilian society: COVID-19 spread and Legal Amazon deforestation. Our approach is able to automatically analyse structured domain data, select relevant
content, generate news texts and publish them on the Web. We provide a thorough description of our system architecture, report on the results of automatic evaluation, discuss some of the advantages of robot-journalism in society, and point out further steps in our work. Corpus and code are publicly available.

Towards Fully Automated News Reporting in Brazilian Portuguese

Even though social networks can provide free space for discussing ideas, people can also use them to propagate Hate Speech and, given the amount of written material in such networks, it becomes necessary to rely on automatic methods for identifying this problem. In this work, we set out to verify the use of some classic Machine Learning algorithms for the task of Hate Speech detection in tweets written in Portuguese, by testing four different models (SVM, MLP, Logistic Regression and Naïve Bayes) with different configurations

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