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A Classification Module for Genetic Programming Algorithms in JCLEC
(MIT Press, 2014)
JCLEC-Classi cation is a usable and extensible open source library for genetic program-
ming classi cation algorithms. It houses implementations of rule-based methods for clas-
si cation based on genetic programming, ...
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
(2017-01-19)
Individuals from Pittsburgh rule-based classifiers represent a complete solution
to the classification problem and each individual is a variable-length set
of rules. Therefore, these systems usually demand a high level ...
Speeding up Multiple Instance Learning Classification Rules on GPUs
(2017)
Multiple instance learning is a challenging task in supervised learning and data mining. How-
ever, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets.
Graphics processing ...
Speeding Up Evolutionary Learning Algorithms using GPUs
(ESTYLF, 2010)
This paper propose a multithreaded Genetic
Programming classi cation evaluation model
using NVIDIA CUDA GPUs to reduce the
computational time due to the poor perfor-
mance in large problems. Two di erent clas-
si ...
Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
(Elsevier, 2020)
The wide availability of specific courses together with the flexibility of academic plans in university
studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear
as tools that help ...
Data mining in predictive maintenance systems: A taxonomy and systematic review
(Wiley, 2022)
Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored ...
MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning
(Elsevier, 2022)
MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities ...