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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.

BRACIS 2020

Sample Bias Effect on Meta-learning

sample bias

automl

meta-learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 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.

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

Hate Speech Detection in Portuguese with Naïve Bayes, SVM, MLP and Logistic Regression

The Brazilian Digital Library of Thesis and Dissertations (BDTD) provides essential data to support many social sciences investigations. Nevertheless, there is still a lack of computation tools tailored for helping extract and analyze the necessary information from the BDTD library. In this paper, we discuss the development of computational solutions to answer questions from a particular social sciences research using metadata from BDTD. The solutions involve the integration of data processing and presentation techniques, such as string-processing algorithms, knowledge graphs and information visualizations. All programming codes implemented at the scope of the project are available for helping other researchers. The paper also highlights the importance of having researchers from Social Science and Computer Science working together, what motivates future collaborations in these areas.

Computational Mining on IBICT BDTD’s Thesis and Dissertation Metadata for Supporting Social Science Research

Glaucoma is a significant cause of blindness in the world. Doctors use computerized images to detect these diseases. Early detection of the disease increases the chances of treatment, reducing the adverse effects. This work proposes an evaluation of texture maps combinations as input to Convolutional Neural Networks for glaucoma classification in retinal images. In our experiments, we used three textures maps, three CNN architectures, and three classifiers. We achieve a Kappa = 0.708+-0.054 and a Accuracy = 0.859+-0.021. We conclude that using the combination of texture maps can improve the automatic detection of glaucoma compared to single-channel inputs, and could be used by state-of-the-art methods to improve their classification rates.

Sample Bias Effect on Meta-learning

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Sample Bias Effect on Meta-learning

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

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Meta Soft Label Generation for Noisy Labels

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