Feature extraction by grammatical evolution for one-class time series classification
نویسندگان
چکیده
منابع مشابه
Feature Extraction for One-Class Classification
Feature reduction is often an essential part of solving a classification task. One common approach for doing this, is Principal Component Analysis. There the low variance directions in the data are removed and the high variance directions are retained. It is hoped that these high variance directions contain information about the class differences. For one-class classification or novelty detecti...
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A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. TSF employs a combination of entropy gain and a distance measure, referred to as the Entrance (entropy and distance) gain, for evaluating the splits. Experimental studies show that the Entrance gain improves the accuracy of TSF. TSF randomly samples features at each tree node and has com...
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We suggest a simple yet effective and parameter-free feature construction process for time series classification. Our process is decomposed in three steps: (i) we transform original data into several simple representations; (ii) on each representation, we apply a coclustering method; (iii) we use coclustering results to build new features for time series. It results in a new transactional (i.e....
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We present a flexible, general-purpose technique for generating time series classifiers. These classifiers are two-stage algorithms; each consists of a set of feature extraction programs, used for transforming the time series into a vector of descriptive scalar features, and a back-end classifier (such as a support vector machine) which uses these features to predict a label. We use grammars to...
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The traditional models of price, and its statistical signatures are often based on limiting assumptions, such as linearity. Moreover, the model developer is faced with the model selection problem, and model uncertainty. In this paper we introduce a method based on Grammatical Evolution (GE) to evolve models for predicting financial returns, and we examine the profitability of these models. Our ...
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ژورنال
عنوان ژورنال: Genetic Programming and Evolvable Machines
سال: 2021
ISSN: 1389-2576,1573-7632
DOI: 10.1007/s10710-021-09403-x