Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
On Issues of Instance Selection
Data Mining and Knowledge Discovery
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Book review: Three perspectives of data mining
Artificial Intelligence
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection
Pattern Recognition Letters
A review of instance selection methods
Artificial Intelligence Review
Towards a better understanding of incremental learning
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
An incremental hypersphere learning framework for protein membership prediction
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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Recent progress in sensing, networking and data management has led to a wealth of valuable information. The challenge is to extract meaningful knowledge from such data produced at an astonishing rate. Unlike batch learning algorithms designed under the assumptions that data is static and its volume is small (and manageable), incremental algorithms can rapidly update their models to incorporate new information (on a sample-by-sample basis). In this paper we propose a new incremental instance-based learning algorithm which presents good properties in terms of multi-class support, complexity, scalability and interpretability. The Incremental Hypersphere Classifier (IHC) is tested in well-known benchmarks yielding good classification performance results. Additionally, it can be used as an instance selection method since it preserves class boundary samples.