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Evolving Classifiers: Methods for Incremental Learning
Greg Hulley,Tshilidzi Marwala +1 more
TL;DR: A comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic Algorithm (ILUGA), which has shown good incremental learning capabilities on benchmark datasets on which the new ILUGA method has been tested.
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Abstract: 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 learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent incremental learning algorithms, and the new proposed method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has shown good incremental learning capabilities on benchmark datasets on which the new ILUGA method has been tested. ILUGA has also shown good incremental learning ability using only a few classifiers and does not suffer from catastrophic forgetting. The results obtained for ILUGA on the Optical Character Recognition (OCR) and Wine datasets are good, with an overall accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT for the difficult multi-class OCR dataset.
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Figures
FIGURE 1. The optimization of the SVM using genetic algorithm 
TABLE 5. Wine Recognition Dataset ![TABLE 6. Learn++ MT performance on the Wine Recognition Dataset[10]](/figures/table6-1-56ss8vpc74m7.png)
TABLE 6. Learn++ MT performance on the Wine Recognition Dataset[10] 
FIGURE 2. Voting using unique weights going to the weighted majority vote 
TABLE 4.ILUGA performance results on OCR database ![TABLE 3.Learn++.MT performance results on OCR database[10]](/figures/table3-1-qw11hahki1ze.png)
TABLE 3.Learn++.MT performance results on OCR database[10]
![TABLE 6. Learn++ MT performance on the Wine Recognition Dataset[10]](/figures/table6-1-56ss8vpc74m7.webp)
![TABLE 3.Learn++.MT performance results on OCR database[10]](/figures/table3-1-qw11hahki1ze.webp)
Citations
Incremental supervised learning: algorithms and applications in pattern recognition
TL;DR: An overview of the main concepts and supervised algorithms of incremental learning, including a synthesis of research studies done in this field and focusing on neural networks, decision trees and support vector machines are presented.
32
Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm
TL;DR: In this paper , the authors implemented an automatic procedure to weekly detect new SARS-CoV-2 variants and non-neutral variants (variants of concern (VOC) and variants of interest (VOI)).
Traffic classification for connectionless services with incremental learning
V. Punitha,C. Mala +1 more
TL;DR: The simulation results show that the proposed incremental learning strategy improves the classification accuracy of the proposed hybrid classifier compared to existing traditional learning methods.
6
Review of the application of machine learning to the automatic semantic annotation of images
Abass A. Olaode,Golshah Naghdy +1 more
TL;DR: The role of machine learning in bridging the semantic gap in content-based image retrieval is explained, an automatic image annotation framework is proposed, in which training images are obtained from social media, and semantic indexing is achieved using a combination of supervised and unsupervised machine learning.
6
Streaming data classification
P. V Srilakshmi Annapoorna,T. T. Mirnalinee +1 more
- 08 Apr 2016
TL;DR: A novel algorithm has been implemented using Random Forest with stratified random sampling and Bloom filtering in order to reduce the training time and to handle high velocity data.
6
References
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The Nature of Statistical Learning Theory
Vladimir Vapnik
- 01 Jan 1995
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
46K
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf,Alexander J. Smola +1 more
- 01 Dec 2001
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
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