Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining features for sequence classification
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Machine Learning
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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The Journal of Machine Learning Research
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Knowledge-Based Systems
Proceedings of the VLDB Endowment
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Knowledge-Based Systems
Information Sciences: an International Journal
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Computer Music Journal
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Information Sciences: an International Journal
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Feature selection using structural similarity
Information Sciences: an International Journal
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
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In this paper, we propose a VECtor DIScovery approach, called VECDIS, which enhances the learning performance of existing classifiers directly from various data types and is able to discover features made of multiple feature types for explanatory purposes. The data types could be combinations of multivariate, short time-series or short sequential data. The features in the dataset could have single item or/and a list of ordered items of different sizes. The present approach allows handling raw vector data without prior manipulation (i.e., preprocessing). The discovered features are made of vector and non-vector mathematical relations. The algorithm generates new vector features and mathematical expression features that are transmitted or exchanged with previously generated features, to the next iterative step. The approach is able to search and automatically discover thousands of different features (sequence manipulation), performed on the sequence features. We performed large number of experiments with various synthetic and simulated datasets and with a wide range of classifiers. The results show that VECDIS enhanced significantly the classification performance of existing classifiers to handle datasets having multiple feature types with short sequence features. Nevertheless, there is no guarantee that the mathematical library as presented in this paper is suitable to all sequence datasets and would lead to discovering a valuable feature set. Therefore, VECDIS enables expanding or exchanging the mathematical library as desire.