Now showing items 1-5 of 5
Multi-Objective Genetic Programming for Feature Extraction and Data Visualization
Feature extraction transforms high dimensional data into a new subspace of lower dimensionalitywhile keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. ...
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, ...
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
Supervised discretization is one of basic data preprocessing techniques used in data mining. CAIM (Class- Attribute InterdependenceMaximization) is a discretization algorithm of data for which the classes are known. ...
Parallel evaluation of Pittsburgh rule-based classifiers on GPUs
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
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 ...