Skip to main navigation
Skip to search
Skip to main content
University of Birmingham Home
Help & FAQ
Home
Research output
Profiles
Research units
Projects
Activities
Datasets
Equipment
Prizes
Press/Media
Search by expertise, name or affiliation
Multiobjective genetic programming for maximizing ROC performance
Pu Wang
, Ke Tang
, Thomas Weise
, E.P.K. Tsang
,
Xin Yao
Computer Science
Research output
:
Contribution to journal
›
Article
›
peer-review
29
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Multiobjective genetic programming for maximizing ROC performance'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Binary Classification
20%
C4.5
20%
Characteristic Graph
20%
Characteristic Performance
100%
Characteristic Space
20%
Classification Problem
20%
Convex Hull
20%
Dominated Hypervolume
20%
Evolutionary multi-objective
20%
False Positive Rate
40%
Local Search Algorithm
20%
Local Search Operator
20%
Machine Learning Algorithms
20%
Many-objective Evolutionary Algorithm (MaOEA)
20%
Memetic Approach
20%
Multi-objective Framework
20%
Multi-objective Genetic Programming
100%
Multi-objective Selection
20%
Naïve Bayes
20%
Non-dominated Sorting Genetic Algorithm (NSGA-II)
20%
Objective Algorithms
20%
Programming Framework
20%
Receiver Operating Characteristic
100%
Result-oriented
20%
ROC Convex Hull
40%
Single-objective
20%
SMS-EMOA
20%
Traditional Machine Learning
20%
True Positive Rate
40%
UCI Dataset
20%
Computer Science
Approximation (Algorithm)
14%
Binary Classification
14%
Classification Problem
28%
Experimental Result
14%
False Positive Rate
28%
Genetic Algorithm
14%
Genetic Programming
100%
Machine Learning Algorithm
14%
multi-objective evolutionary algorithm
14%
Multiobjective
100%
Performance Characteristic
100%
Programming Framework
14%
Search Strategies
14%
Single Objective
14%
True Positive Rate
28%