Fundamentals of speech recognition
Fundamentals of speech recognition
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Nonlinear time series analysis
Nonlinear time series analysis
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining in Time Series Database
Data Mining in Time Series Database
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
A Data Mining Based Approach for the EEG Transient Event Detection and Classification
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Open source clustering software
Bioinformatics
Structural Periodic Measures for Time-Series Data
Data Mining and Knowledge Discovery
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
Characteristic-Based Clustering for Time Series Data
Data Mining and Knowledge Discovery
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Mining for similarities in time series data using wavelet-based feature vectors and neural networks
Engineering Applications of Artificial Intelligence
Intelligent stock trading system by turning point confirming and probabilistic reasoning
Expert Systems with Applications: An International Journal
A review of feature selection techniques in bioinformatics
Bioinformatics
An Efficient Similarity Searching Algorithm Based on Clustering for Time Series
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Cluster Analysis
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
A time series representation model for accurate and fast similarity detection
Pattern Recognition
Engineering Applications of Artificial Intelligence
Clustering of time series data-a survey
Pattern Recognition
Clustering signals using wavelets
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Blind feature extraction for time-series classification using haar wavelet transform
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Time series feature evaluation in discriminating preictal EEG states
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Empirical comparison of clustering methods for long time-series databases
AM'03 Proceedings of the Second international conference on Active Mining
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
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We propose a forward sequential feature selection scheme based on k-means clustering algorithm to derive the feature subset that classifies best the time series data base, according to the criterion of the corrected Rand index. Moreover, we investigate the effect of the standardization scheme on the feature selection and propose a standardization given by the transform to standard Gaussian distribution. Our interest in this work is in classification of oscillating dynamical systems on the basis of measures computed on time series from these systems. The features to be selected are measures of linear and non-linear analysis of time series, such as auto-correlation and Lyapunov exponents, as well as oscillation characteristics, such as the mean magnitude of peaks. Simulations on known oscillating deterministic and stochastic systems showed that, for repeated realizations of the same classification task, the proposed feature selection scheme selected very often the same best feature subset, giving high classification accuracy for any standardization. We found that, regardless of the standardization, the highest classification accuracy could be obtained with a small feature subset, containing most frequently an oscillating-related feature. The same setting was applied to records of epileptic electroencephalogram signals, giving varying results and dependent on the standardization. © 2012 Wiley Periodicals, Inc.