Efficient Feature Selection via CMA-ES (Covariance Matrix Adaptation Evolution Strategy) explores the challenge of feature selection in model building for large datasets. With a particular focus on using evolutionary algorithms, this article introduces SFS (Sequential Feature Search) as a baseline technique and delves into a more complex approach – CMA-ES (Covariance Matrix Adaptation Evolution Strategy). Both methods are applied to a dataset to minimize the Bayesian Information Criterion (BIC) as the objective function. The article also highlights the performance and efficiency of CMA-ES compared to SFS. Part 2 of this feature selection series is forthcoming. For further details, refer to the complete code and additional resources in the provided links.
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Efficient Feature Selection via CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
Using evolutionary algorithms for fast feature selection with large datasets
This is part 1 of a two-part series about feature selection. Part 2 will be linked here when it’s published.
When fitting a model to a dataset, feature selection may be necessary for various reasons such as model explainability, avoiding the curse of dimensionality, or maximizing/minimizing objective functions. When the number of features is large, an exhaustive search becomes impractical, and a heuristic method is required to efficiently explore the search space and find the optimal feature combination.
SFS — Sequential Feature Search
SFS is a simple algorithm for feature selection that is reasonably fast even with large feature sets. It uses a greedy approach to iteratively select features that minimize the objective function.
CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
CMA-ES is a numeric optimization algorithm that is computationally efficient and used in various numeric optimization libraries. It is particularly useful for high-dimensional, discrete search spaces, such as feature selection problems. The algorithm iteratively modifies distribution parameters to find the best objective function values.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Efficient feature selection via CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
- Towards Data Science – Medium
- Twitter – @itinaicom
