Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
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Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enhanced 1-NN time series classification using badness of records
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Time series shapelets: a new primitive for data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machines of interval-based features for time series classification
Knowledge-Based Systems
A review on time series data mining
Engineering Applications of Artificial Intelligence
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Data Mining and Knowledge Discovery
Feature detection from illustration of time-series data
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Decision forest: an algorithm for classifying multivariate time series
International Journal of Business Intelligence and Data Mining
Order-Preserving sparse coding for sequence classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Invariant time-series classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
A time series forest for classification and feature extraction
Information Sciences: an International Journal
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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This work presents decision trees adequate for the classification of series data. There are several methods for this task, but most of them focus on accuracy. One of the requirements of data mining is to produce comprehensible models. Decision trees are one of the most comprehensible classifiers. The use of these methods directly on this kind of data is, generally, not adequate, because complex and inaccurate classifiers are obtained. Hence, instead of using the raw features, new ones are constructed.This work presents two types of trees. In interval-based trees, the decision nodes evaluate a function (e.g., the average) in an interval and the result is compared to a threshold. For DTW-based trees each decision node has a reference example. The distance from the example to classify to the reference example is calculated and then it is compared to a threshold.The method for obtaining these trees it is based on 1) to develop a method that obtains for a 2-class data set a classifier formed by a new feature (a function in an interval or the distance to a reference example) and a threshold, 2) to use the boosting method to obtain an ensemble of these classifiers, and 3) to use a method for constructing decision trees using as data set the features selected by boosting.