A comprehensive survey on support vector machine classification: Applications, challenges and trends
In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …
machines (SVMs) and their application in several fields of science. SVMs are one of the …
A survey on evolutionary machine learning
Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that
function like humans. AI has been applied to many real-world applications. Machine …
function like humans. AI has been applied to many real-world applications. Machine …
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its
good theoretic foundations and high classification accuracy, normal SVM is not suitable for …
good theoretic foundations and high classification accuracy, normal SVM is not suitable for …
FeSA: Feature selection architecture for ransomware detection under concept drift
DW Fernando, N Komninos - Computers & Security, 2022 - Elsevier
This paper investigates how different genetic and nature-inspired feature selection
algorithms operate in systems where the prediction model changes over time in unforeseen …
algorithms operate in systems where the prediction model changes over time in unforeseen …
Class imbalance learning using fuzzy ART and intuitionistic fuzzy twin support vector machines
The classification in imbalanced datasets is one of the main problems for machine learning
techniques. Support vector machine (SVM) is biased to the majority class samples, and the …
techniques. Support vector machine (SVM) is biased to the majority class samples, and the …
Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework
Since knowledge in an expert system is vague and modified frequently, expert systems are
fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework …
fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework …
A review of evolutionary algorithms for data mining
AA Freitas - Data Mining and Knowledge Discovery Handbook, 2010 - Springer
Summary Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …
process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that …
Large scale data mining using genetics-based machine learning
We are living in the peta-byte era. We have larger and larger data to analyze, process and
transform into useful answers for the domain experts. Robust data mining tools, able to cope …
transform into useful answers for the domain experts. Robust data mining tools, able to cope …
EACD: evolutionary adaptation to concept drifts in data streams
This paper presents a novel ensemble learning method based on evolutionary algorithms to
cope with different types of concept drifts in non-stationary data stream classification tasks. In …
cope with different types of concept drifts in non-stationary data stream classification tasks. In …
A survey of statistical machine learning elements in genetic programming
Modern genetic programming (GP) operates within the statistical machine learning (SML)
framework. In this framework, evolution needs to balance between approximation of an …
framework. In this framework, evolution needs to balance between approximation of an …