A Stability-Aware Hybrid Feature Selection Algorithm Based on Filtered Sparsity and Wrapper Refinement

Rashidov Khusan Shirinboyevich

doi:10.29013/ajt-26-1.2-55-62

February 28, 2026Austrian Journal of Technical and Natural SciencesOpen Access

A Stability-Aware Hybrid Feature Selection Algorithm Based on Filtered Sparsity and Wrapper Refinement

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Key Points

The aim is to develop a feature selection algorithm that enhances stability and performance in supervised machine learning tasks.
Developed a hybrid feature selection algorithm combining filtered sparsity with wrapper refinement.
Applied the algorithm to various datasets of medium to high dimensionality.
Analyzed the impact of irrelevant and interrelated predictors on model performance.
The hybrid algorithm showed improved stability in feature selection compared to traditional methods.
Significant reduction in uninformative features was achieved, enhancing model accuracy.

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Implication

Demonstrates a new feature selection algorithm in supervised machine learning, implying improved model performance.

Abstract

The process of choosing relevant features is essential in supervised machine learning, especially when dealing with tabular data of medium to high dimensions where unnecessary, uninformative, and interrelated predictors can negatively impact how well a mo

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Rashidov Khusan Shirinboyevich (2026) studied this question.

synapsesocial.com/papers/69ccb69d16edfba7beb8857e https://doi.org/10.29013/ajt-26-1.2-55-62

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A Stability-Aware Hybrid Feature Selection Algorithm Based on Filtered Sparsity and Wrapper Refinement

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

A Stability-Aware Hybrid Feature Selection Algorithm Based on Filtered Sparsity and Wrapper Refinement

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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