Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Proceedings of the VLDB Endowment
Particle swarm optimization for prototype reduction
Neurocomputing
Nearest neighbors in high-dimensional data: the emergence and influence of hubs
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A hybrid immune simulated annealing algorithm for the job shop scheduling problem
Applied Soft Computing
A review of instance selection methods
Artificial Intelligence Review
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Data Mining and Knowledge Discovery
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
The Journal of Machine Learning Research
Weighted dynamic time warping for time series classification
Pattern Recognition
INSIGHT: efficient and effective instance selection for time-series classification
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Segment-Based Features for Time Series Classification
The Computer Journal
Large margin mixture of AR models for time series classification
Applied Soft Computing
Using derivatives in time series classification
Data Mining and Knowledge Discovery
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We propose a new immune binary particle swarm optimization algorithm (IBPSO) to solve the problem of instance selection for time series classification, whose objective is to find out the smallest instance combination with maximal classification accuracy. The proposed IBPSO is based on the basic binary particle swarm optimization (BPSO) algorithm proposed by Kennedy and Eberhart. Its immune mechanism includes vaccination and immune selection. Vaccination employs the hubness score of time series and the particles' inertance as heuristic information to direct the search process. Immune selection procedure always discards the particle with the worst fitness in the current swarm for preventing the degradation of the swarm. Experimental results on small and medium datasets show that IBPSO outperforms BPSO and deterministic INSIGHT in terms of storage requirement and classification accuracy, and presents better robustness to noise than BPSO. In addition, experimental results on larger datasets indicate that IBPSO has better scalability than BPSO.