Feature Selection for Multi-label Learning

• Grant number: 11/02393-4
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2011
• End date: July 31, 2014
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Maria Carolina Monard
• Grantee: Newton Spolaôr
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 12/23906-2 - Feature selection for multi-label learning, BE.EP.DR

Scientific publications

(10)

(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)

SPOLAOR, NEWTON; MONARD, MARIA CAROLINA; TSOUMAKAS, GRIGORIOS; LEE, HUEI DIANA. A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, v. 180, n. SI, p. 3-15, MAR 5 2016. (11/02393-4)

TOMAS, JIMENA TORRES; SPOLAOR, NEWTON; CHERMAN, EVERTON ALVARES; MONARD, MARIA CAROLINA. A Framework to Generate Synthetic Multi-label Datasets. ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, v. 302, p. 22-pg., 2014-02-25. (11/12597-6, 10/15992-0, 11/02393-4)

SPOLAOR, NEWTON; CHERMAN, EVERTON ALVARES; MONARD, MARIA CAROLINA; LEE, HUEI DIANA; POZO, ATR; CAMARGO, HD; FURTADO, V; PINHEIRO, V. ReliefF for Multi-label Feature Selection. 2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), v. N/A, p. 6-pg., 2013-01-01. (11/02393-4, 10/15992-0)

CHERMAN, EVERTON ALVARES; SPOLAOR, NEWTON; VALVERDE-REBAZA, JORGE; MONARD, MARIA CAROLINA. Lazy Multi-label Learning Algorithms Based on Mutuality Strategies. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 80, p. 16-pg., 2015-12-01. (13/12191-5, 10/15992-0, 11/02393-4, 11/22749-8)

SPOLAOR, NEWTON; MONARD, MARIA CAROLINA; TSOUMAKAS, GRIGORIOS; LEE, HUEI DIANA. A systematic review of multi-label feature selection and a new method based on label construction. Neurocomputing, v. 180, p. 13-pg., 2016-03-05. (11/02393-4)

SPOLAOR, NEWTON; LEE, HUEI DIANA; RESENDE TAKAKI, WEBER SHOITY; WU, FENG CHUNG. Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, v. 8, p. 13-pg., 2015-12-11. (11/02393-4)

SPOLAOR, NEWTON; LEE, HUEI DIANA; RESENDE TAKAKI, WEBER SHOITY; WU, FENG CHUNG. Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, v. 8, n. 2, SI, p. 3-15, DEC 11 2015. (11/02393-4)

CHERMAN, EVERTON ALVARES; SPOLAOR, NEWTON; VALVERDE-REBAZA, JORGE; MONARD, MARIA CAROLINA. Lazy Multi-label Learning Algorithms Based on Mutuality Strategies. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v. 80, n. 1, SI, p. S261-S276, DEC 2015. (11/02393-4, 11/22749-8, 13/12191-5, 10/15992-0)

SPOLAOR, NEWTON; BENITTI, FABIANE B. VAVASSORI. Robotics applications grounded in learning theories on tertiary education: A systematic review. COMPUTERS & EDUCATION, v. 112, p. 97-107, SEP 2017. (11/02393-4)

SPOLAOR, NEWTON; MONARD, MARIA CAROLINA; LEE, HUEI DIANA; YANG, Q; WOOLDRIDGE, M. Feature Selection for Multi-Label Learning. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), v. N/A, p. 2-pg., 2015-01-01. (11/02393-4)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SPOLAÔR, Newton. Feature selection for multi-label learning. 2014. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.