Boosting interval based literals

Authors:
Juan J. Rodríguez;Carlos J. Alonso;Henrik Boström
Affiliations:
Escuala Politecnica Superior, 09006 Burgos, Spain;Escuala Politecnica Superior, 09006 Burgos, Spain;Escuala Politecnica Superior, 09006 Burgos, Spain
Venue:
Intelligent Data Analysis
Year:
2001

Citing 14
Cited 6

Finding patterns in time series: a dynamic programming approach

Advances in knowledge discovery and data mining
Improved boosting algorithms using confidence-rated predictions

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Characterization of the Sonar Signals Benchmark

Neural Processing Letters
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach

Data Mining and Knowledge Discovery
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Perceptron Learning Revisited: The Sonar Targets Problem

Neural Processing Letters
Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Using output codes to boost multiclass learning problems

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Comprehensible Descriptions of Multivariate Time Series

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Brief Introduction to Boosting

IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Local feature extraction and its applications using a library of bases

Local feature extraction and its applications using a library of bases

A comparison of two machine-learning techniques to focus the diagnosis task

Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Support vector machines of interval-based features for time series classification

Knowledge-Based Systems
A brief survey on sequence classification

ACM SIGKDD Explorations Newsletter
Extending adaboost to iteratively vary its base classifiers

Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Classification trees for time series

Pattern Recognition
A time series forest for classification and feature extraction

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

A supervised classification method for time series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of predicates are used: i) relative predicates, such as "increases" and "stays", and ii) region predicates, such as "always" and "sometime", which operate over regions in the domain of the variable. Experiments on different data sets, several of them obtained from the UCI ML and KDD repositories, show that the proposed method is highly competitive with previous approaches.