Advanced machine learning

• Grant number: 16/18615-0
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• Start date: September 01, 2017
• End date: February 29, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Agreement: IBM Brasil
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: André Carlos Ponce de Leon Ferreira de Carvalho
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• City of the host institution: São Carlos
• Company: IBM Brasil - Indústria, Máquinas e Serviços Ltda
• Company city: Rio de Janeiro
• Associated researchers: Ana Carolina Lorena ; Gustavo Enrique de Almeida Prado Alves Batista ; Moacir Antonelli Ponti ; Paula Costa Castro ; Renato Tinós ; Ricardo Cerri

Abstract

Without being aware, we are using technologies based on Machine learning (ML) algorithms in a growing number of our daily activities. The use of ML has made many risk and tiring activities safer, more reliable and more accurate. Despite these contributions, new demands require the development of new ML algorithms, or use of these algorithms in new and innovative ways. Two important current demands are to efficiently deal data that arrive in streams, where novelties can appear and concepts can change, and how to improve the use of the most appropriate ML algorithms, and the adequate values for the hyper-parameters of the algorithms selected for a new task. This project will investigate new approaches to efficiently deal with these demands. (AU)

Scientific publications

(19)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

PADILHA, VICTOR A.; ALKHNBASHI, OMER S.; SHAH, SHIRAZ A.; DE CARVALHO, ANDRE C. P. L. F.; BACKOFEN, ROLF. CRISPRcasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems. GIGASCIENCE, v. 9, n. 6, JUN 2020. (16/18615-0, 13/07375-0, 19/21300-9)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. E.; IEEE. Statistical versus Distance-Based Meta-Features for Clustering Algorithm recommendation Using Meta-Learning. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2018-01-01. (12/22608-8, 17/20265-0, 16/18615-0)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. E.; IEEE. Unsupervised Meta-Learning for Clustering Algorithm Recommendation. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2019-01-01. (12/22608-8, 17/20265-0, 16/18615-0)

AGUIAR, GABRIEL JONAS; MANTOVANI, RAFAEL GOMES; MASTELINI, SAULO M.; DE CARVALHO, ANDRE C. P. F. L.; CAMPOS, GABRIEL F. C.; BARBON JUNIOR, SYLVIO. A meta-learning approach for selecting image segmentation algorithm. PATTERN RECOGNITION LETTERS, v. 128, p. 480-487, DEC 1 2019. (16/18615-0, 12/23114-9, 13/07375-0, 18/07319-6)

PADILHA, VICTOR A.; DE CARVALHO, ANDRE C. P. L. F.; IEEE. A Study of Biclustering Coherence Measures for Gene Expression Data. 2018 7TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), v. N/A, p. 6-pg., 2018-01-01. (16/18615-0, 17/02975-0, 13/07375-0)

SPADON, GABRIEL; DE CARVALHO, ANDRE C. P. L. F.; RODRIGUES-JR, JOSE F.; ALVES, LUIZ G. A.. Reconstructing commuters network using machine learning and urban indicators. SCIENTIFIC REPORTS, v. 9, AUG 13 2019. (16/17078-0, 17/08376-0, 19/04461-9, 13/07375-0, 16/16987-7, 16/18615-0, 14/25337-0)

GARCIA, LUIS P. F.; LEHMANN, JENS; DE CARVALHO, ANDRE C. P. L. F.; LORENA, ANA C.. New label noise injection methods for the evaluation of noise filters. KNOWLEDGE-BASED SYSTEMS, v. 163, p. 693-704, JAN 1 2019. (16/18615-0, 13/07375-0, 12/22608-8)

BARELLA, VICTOR H.; GARCIA, LUIS P. F.; DE SOUTO, MARCILIO P.; LORENA, ANA C.; DE CARVALHO, ANDRE; IEEE. Data Complexity Measures for Imbalanced Classification Tasks. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2018-01-01. (16/18615-0, 13/07375-0, 15/01382-0)

TINOS, RENATO; YANG, SHENGXIANG. A framework for inducing artificial changes in optimization problems. INFORMATION SCIENCES, v. 485, p. 486-504, JUN 2019. (16/18615-0, 15/06462-1, 13/07375-0)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. F.. A Meta-learning approach for recommending the number of clusters for clustering algorithms. KNOWLEDGE-BASED SYSTEMS, v. 195, MAY 11 2020. (16/18615-0, 17/20265-0, 12/22608-8)

PADILHA, VICTOR ALEXANDRE; DE LEON FERREIRA DE CARVALHO, ANDRE CARLOS PONCE. Experimental correlation analysis of bicluster coherence measures and gene ontology information. APPLIED SOFT COMPUTING, v. 85, DEC 2019. (16/18615-0, 13/07375-0, 17/02975-0)

PADILHA, VICTOR A.; ALKHNBASHI, OMER S.; SHAH, SHIRAZ A.; DE CARVALHO, ANDRE C. P. L. F.; BACKOFEN, ROLF. CRISPRcasIdentifier: Machine learning for accurate identification and classification of CRISPR-Cas systems. GIGASCIENCE, v. 9, n. 6, p. 12-pg., 2020-06-01. (13/07375-0, 19/21300-9, 16/18615-0)

TINOS, RENATO; WHITLEY, DARRELL; OCHOA, GABRIELA. A New Generalized Partition Crossover for the Traveling Salesman Problem: Tunneling between Local Optima. EVOLUTIONARY COMPUTATION, v. 28, n. 2, p. 255-288, JUN 2020. (16/18615-0, 15/06462-1, 13/07375-0)

PIMENTEL, BRUNO ALMEIDA; DE CARVALHO, ANDRE C. P. L. F.. A new data characterization for selecting clustering algorithms using meta-learning. INFORMATION SCIENCES, v. 477, p. 203-219, MAR 2019. (13/07375-0, 16/18615-0, 17/20265-0)

RIVOLLI, ADRIANO; READ, JESSE; SOARES, CARLOS; PFAHRINGER, BERNHARD; DE CARVALHO, ANDRE C. P. L. F.. An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. MACHINE LEARNING, v. 109, n. 8, JUN 2020. (16/18615-0, 13/07375-0, 12/22608-8)

PADILHA, VICTOR A.; DE CARVALHO, ANDRE C. P. L. F.; IEEE. A Comparison of Hierarchical Biclustering Ensemble Methods. 2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), v. N/A, p. 6-pg., 2017-01-01. (13/07375-0, 17/02975-0, 16/18615-0)

GARCIA, LUIS P. F.; RIVOLLI, ADRIANO; ALCOBACA, EDESIO; LORENA, ANA C.; DE CARVALHO, ANDRE C. P. L. F.. Boosting meta-learning with simulated data complexity measures. Intelligent Data Analysis, v. 24, n. 5, p. 1011-1028, 2020. (12/22608-8, 13/07375-0, 18/14819-5, 16/18615-0)

ALCOBACA, EDESIO; SIQUEIRA, FELIPE; RIVOLLI, ADRIANO; GARCIA, LUIS P. F.; OLIVA, JEFFERSON T.; DE CARVALHO, ANDRE C. P. L. F.. MFE: Towards reproducible meta-feature extraction. JOURNAL OF MACHINE LEARNING RESEARCH, v. 21, 2020. (13/07375-0, 18/14819-5, 16/18615-0)

RIVOLLI, ADRIANO; GARCIA, LUIS P. F.; SOARES, CARLOS; VANSCHOREN, JOAQUIN; DE CARVALHO, ANDRE C. P. L. F.. Meta-features for meta-learning. KNOWLEDGE-BASED SYSTEMS, v. 240, p. 21-pg., 2022-03-15. (13/07375-0, 16/18615-0)