Ronaldo C. Prati

Universidade Federal do ABC

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Maria Carolina Monard

Universidade de São Paulo

Francisco Herrera

Universidad de Granada

Alberto Fernández

Universidad de Granada

Salvador García

Universidad de Granada

Bartosz Krawczyk

Virginia Commonwealth University

Mikel Galar

Universidad Publica de Navarra

Carlos Kamienski

Universidade Federal do ABC

Gustavo Batista

UNSW Sydney

Juarez L. F. Da Silva

Universidade de São Paulo

Marcos G. Quiles

Universidade Federal de São Paulo

Cooperative Institutions

Universidade Federal do ABC

Universidade de São Paulo

Brazilian Society of Computational and Applied Mathematics

Universidade Federal de São Carlos

Universidade Estadual de Campinas (UNICAMP)

National Institute for Space Research

University of Bologna

Brazilian Agricultural Research Corporation

Universidade Federal de São Paulo

Universidade Federal de Pernambuco

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Aprendizagem ativa em fluxo de dados com latência intermediária

Pedro Henrique Parreira Ronaldo C. Prati

Um fluxo de dados é caracterizado por uma produção massiva de exemplos devido à chegada contínua desses exemplos. Em problemas de classificação em fluxo de dados, normalmente o rótulo real do exemplo é necessário para a avaliação do desempenho ou a detecção de mudança de conceito. No entanto, em muitas aplicações, a obtenção de todos os rótulos verdadeiros é impraticável devido ao alto custo associado. Apesar de existirem diversos trabalhos que utilizam a aprendizagem ativa em fluxo de dados para obter uma porção de exemplos rotulados, normalmente eles consideram que o rótulo verdadeiro é disponibilizado de forma imediata na requisição, o que nem sempre é possível devido ao tempo necessário de análise do exemplo. Este artigo tem por objetivo a investigação de um novo cenário de fluxo de dados com latência intermediária e restrição de rotulagem, além de propor algumas estratégias de aprendizagem ativa para esse cenário e um framework de suporte teórico para essas estratégias.

10.5753/eniac.2019.9298

Cite

Citations (3)

Constructing ensembles of symbolic classifiers

Flávia Bernardini Maria Carolina Monard Ronaldo C. Prati

Learning algorithms are an integral part of the data mining (DM) process. However, DM deals with a large amount of data and most learning algorithms do not operate in massive datasets. A technique often used to ease this problem is related to data sampling and the construction of ensembles of classifiers. Several methods to construct such ensembles have been proposed. However, these methods often lack an explanation facility. This paper proposes methods to construct ensembles of symbolic classifiers. These ensembles can be further explored in order to explain their decisions to the user. These methods were implemented in the ELE system, also described in this work. Experimental results in two out of three datasets show improvement over all base-classifiers. Moreover, according to the obtained results, methods based on single rule classification might be used to improve the explanation facility of ensembles.

Base (topology)

10.1109/ichis.2005.31

Cite

Citations (14)

Imbalanced Classification for Big Data

Springer eBooks (2018)

Alberto Fernández Salvador García Mikel Galar Ronaldo C. Prati Bartosz Krawczyk

10.1007/978-3-319-98074-4_13

Cite

Citations (8)

Missing Value Imputation Using a Semi-supervised Rank Aggregation Approach

Lecture notes in computer science (2008)

Edson Takashi Matsubara Ronaldo C. Prati Gustavo Batista Maria Carolina Monard

Imputation (statistics)

10.1007/978-3-540-88190-2_27

Cite

Citations (11)

Intelligent Systems

Lecture notes in computer science (2020)

Ricardo Cerri Ronaldo C. Prati

10.1007/978-3-030-61380-8

Cite

Citations (2)

Water spray detection for smart irrigation systems with Mask R-CNN and UAV footage

Caio K. G. Albuquerque Sergio Polimante André Torre-Neto Ronaldo C. Prati

While the world's population rises, demand for food grows accordingly. Smart agriculture emerges as a viable solution to increase the quality and efficiency of crops. Irrigation plays an essential role in the grade and productivity of harvests, while also being a crucial factor in the cost-effectiveness of food production. Smart irrigation uses technology to improve watering, such as the Internet of Things (IoT) applications and Machine Learning algorithms. The correct functioning of irrigation nozzles is critical to ensure that the hydration plan is deployed correctly to the crop field. This paper presents a Machine Learning algorithm that can automatically recognize water from aerial footage of irrigation systems. This automatic recognition can help in the irrigation system inspection, potentially reducing time and cost in system maintenance. Initial results show that it is possible to identify water on image frames captured by an Unmanned Aerial Vehicle (UAV) using the Mask R-CNN Neural Network. The goal is to identify malfunctioning irrigation systems that can lead to under or overwatering, compromising the irrigation plan's correct implementation.

10.1109/metroagrifor50201.2020.9277542

Cite

Citations (22)

Balancing Strategies and Class Overlapping

Lecture notes in computer science (2005)

Gustavo Batista Ronaldo C. Prati Maria Carolina Monard

Data sampling

Training set

Performance Improvement

10.1007/11552253_3

Cite

Citations (89)

Advancing IoT-Based Smart Irrigation

IEEE Internet of Things Magazine (2019)

Rodrigo Togneri Carlos Kamienski Ramide Dantas Ronaldo C. Prati Attilio Toscano

Integrated Internet of Things (IoT) platforms are needed for realizing IoT potential in commercial-scale applications. The main challenge is to provide solution flexibility to meet custom application needs. We developed an IoT-based platform for smart irrigation, with a flexible architecture to easily connect IoT and machine learning (ML) components to build application solutions. Our architecture enables multiple and customizable analytical approaches to precision irrigation, making room for the improvement of ML approaches. Impacts on different stakeholders can be anticipated, including IoT professionals, by facilitating system deployment, and farmers, by providing cost reduction and safer crop yields. Examples are given based on pilots in Europe and Brazil.

SAFER

10.1109/iotm.0001.1900046

Cite

Citations (52)

Extending features for multilabel classification with swarm biclustering

Ronaldo C. Prati Fabrício Olivetti de França

In some data mining applications the analyzed data can be classified as simultaneously belonging to more than one class, this characterizes the multi-label classification problem. Numerous methods for dealing with this problem are based on decomposition, which essentially treats labels (or some subsets of labels) independently and ignores interactions between them. This fact might be a problem, as some labels may be correlated to local patterns in the data. In this paper, we propose to enhance multi-label classifiers with the aid of biclusters, which are capable of finding the correlation between subsets of objects, features and labels. We then construct binary features from these patterns that can be interpreted as local correlations (in terms of subset of features and instances) in the data. These features are used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of some decompositive multi-label learning techniques.

Multi-label classification

Biclustering

10.1109/cec.2013.6557930

Cite

Citations (8)

Improving kNN classification under Unbalanced Data. A New Geometric Oversampling Approach

2022 International Joint Conference on Neural Networks (IJCNN) (2018)

A. M. Carvalho Ronaldo C. Prati

Training classifiers with unbalanced data is one of the main challenges in the field of Machine Learning. Some techniques that try to get around this problem have been proposed, where one of most important is SMOTE, which artificially generates new instances by interpolating pairs of original instances. This paper proposes a new approach to the balancing of classifier training data. It is a geometric and spatial approach, that uses tetrahedralization by Delaunay tessellation to generate new artificial instances. This allow us to go beyond single pair interpolation. Applying this new method, we can notice an improvement in the classification quality (in terms of AUC) of the kNN classification algorithm when compared with SMOTE.

Oversampling

Interpolation

10.1109/ijcnn.2018.8489411

Cite

Citations (7)