The Strength of Weak Learnability
Machine Learning
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Journal of Global Optimization
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Training Recurrent Networks by Evolino
Neural Computation
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Selection of algorithms to solve traveling salesman problems using meta-learning
International Journal of Hybrid Intelligent Systems - Feature and algorithm selection with Hybrid Intelligent Techniques
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
PSO for reservoir computing optimization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
An approach to reservoir computing design and training
Expert Systems with Applications: An International Journal
International Journal of Knowledge-based and Intelligent Engineering Systems
Learning to filter spam emails: An ensemble learning approach
International Journal of Hybrid Intelligent Systems
Boosted Pre-loaded Mixture of Experts for low-resolution face recognition
International Journal of Hybrid Intelligent Systems
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We present here a work that applies an automatic construction of ensembles based on the Clustering and Selection CS algorithm for time series forecasting. The automatic method, called CSELM, initially finds an optimum number of clusters for training data set and subsequently designates an Extreme Learning Machine ELM for each cluster found. For model evaluation, the testing data set are submitted to clustering technique and the nearest cluster to data input will give a supervised response through its associated ELM. Self-organizing maps were used in the clustering phase. Adaptive differential evolution was used to optimize the parameters and performance of the different techniques used in the clustering and prediction phases. The results obtained with the CSELM method are compared with results obtained by other methods in the literature. Five well-known time series were used to validate CSELM.