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.
“`html
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.
As a practical AI solution, Efficient feature selection via CMA-ES can redefine your way of work and help you stay competitive by automating customer engagement, managing interactions, and optimizing sales processes. By identifying automation opportunities, defining KPIs, selecting an AI solution, and implementing gradually, you can leverage AI for measurable impacts on business outcomes.
Spotlight on a Practical AI Solution:
Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
“`
