89 citations · 358 across the 51 of their papers we have counts for
8 papers · 1 filter
Land Cover Mapping Using Ensemble Feature Selection Methods
A. Gidudu, B. Abe, T. Marwala
Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble sy…
The use of entropy to measure structural diversity
L. Masisi, V. Nelwamondo, T. Marwala
In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby speci…
The Effect of Structural Diversity of an Ensemble of Classifiers on Classification Accuracy
Lesedi Masisi, Fulufhelo V. Nelwamondo, Tshilidzi Marwala
This paper aims to showcase the measure of structural diversity of an ensemble of 9 classifiers and then map a relationship between this structural diversity and accuracy. The stru…
Introduction to Relational Networks for Classification
Vukosi Marivate, Tshilidzi Marwala
The use of computational intelligence techniques for classification has been used in numerous applications. This paper compares the use of a Multi Layer Perceptron Neural Network a…
Image Classification Using SVMs: One-against-One Vs One-against-All
Gidudu Anthony, Hulley Gregg, Marwala Tshilidzi
Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory an…
Evolving Classifiers: Methods for Incremental Learning
Greg Hulley, Tshilidzi Marwala
The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental…