# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 1 Introduction  
Machine learning (ML) has achieved a prominent position in recent years. Much of its recent visibility may be attributed to large volumes of data (used for training purposes) as well as improved computing infrastructure [1], giving rise to compelling applications like DALL-E [[https://openai.com/index/dall-e/](https://openai.com/index/dall-e/)]([https://openai.com/index/dall-e/](https://openai.com/index/dall-e/)) (accessed on 13 January 2025) [2] and ChatGPT [[https://openai.com/index/chatgpt/](https://openai.com/index/chatgpt/)]([https://openai.com/index/chatgpt/](https://openai.com/index/chatgpt/)) (accessed on 13 January 2025) [3]. However, this also means greater importance is placed on this step of the ML pipeline than ever before. Data collecting is becoming one of the main bottlenecks in the field. The majority of the time for executing ML end-to-end is spent on preparing the data, which includes gathering, cleaning, analyzing, visualizing, and feature engineering [1]. This only exacerbates already existing related issues, such as the cold-start problem that is observable in certain fields, such as the medical field [4], e-commerce [5], and online education [6]. Manually labeling a dataset is very costly and complex. This is particularly true for research domains like healthcare that have limited access to labeled data [4]. To abstract underlying patterns and create a proper model, approaches often require massive amounts of supervised training data per inherent concept [7]. These learning processes can take days using specialized (and expensive) hardware [7]. One should recognize that there are applications for which data are inherently expensive to acquire, whether due to financial constraints or time limitations, making the development of suitable models for these fields a significant challenge [4]. These challenges mirror similar issues found in the field of digital marketing, where understanding human interaction across the physical and digital worlds requires selecting relevant tools and techniques that ensure flexibility and context-awareness [8; 9]. Multimodal artificial intelligence approaches have demonstrated the importance of integrating diverse data sources to analyze consumer sentiment and enhance engagement [10; 11]. However, the complexity of integrating these modalities underscores the importance of efficient data pre-processing, including cost-effective feature selection, to reduce noise and improve the robustness of ML models [12]. Furthermore, these are not the only problems present in this context. Even if valuable data are sourced to be used for one of these aforementioned fields, current features may be redundant or even irrelevant to the problem at hand, which means that they only add noise to the dataset [12]. In order to deal with these problems, a data engineer may turn their attention to either the data or the algorithms that use them. That is, one may try to use approaches that require less data to obtain results, such as few or even zero-shot algorithms [7]. Or one may dedicate more attention to the data and try to improve them by iteratively cleaning and analyzing them. This work addresses the challenge of balancing effective feature exploration with computational efficiency through a novel data-centric approach. We propose a feature selection method that accelerates the traditionally time-intensive wrapper-based feature selection process by leveraging interim representations of feature combinations. These composite representations enable the method to approximate inter-feature dependencies while relying on filter-based evaluation, resulting in a substantial reduction in computational time without maintaining competitive performance. The core novelty of our approach lies in its ability to deliver wrapper-like effectiveness with filter-level efficiency, making it particularly suitable for resource-constrained machine learning scenarios.