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Application of Grammar Framework to Time-Series Prediction,investigate ways to explore such large feature spaces to extract the best features for prediction, i.e. feature selection (FS). Since the proposed framework involves the generation of a large pool of features, there can be redundant and irrelevant features. Therefore, FS is as equally important as f

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障碍物

Conclusion, used to formalise this hypothesis should be engineered carefully for optimal performance. This is usually done by domain experts which often leads to good results. This brief investigated if an automatic feature generation framework that can generate expert suggested features and many other paramet

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定点

Feature Selection,oices. This problem quickly becomes intractable as . increases. In the literature, suboptimal approaches based on sequential and random searches using evolutionary methods have been proposed and shown to work reasonably well in practice.This chapter describes the mainstream feature selection technique theories.

古董

Grammar Based Feature Generation,lecting features from large feature spaces and selective feature pruning strategies that can be used to contain the most informative features is also presented. The importance of feature selection in a feature generation framework is highlighted.

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Conclusion, good results. This brief investigated if an automatic feature generation framework that can generate expert suggested features and many other parametrized features can be used to improve the performance of ML methods in time-series prediction.

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