Relief-Based Feature Selection: Introduction and Review (1711.08421v2)

Abstract: Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computationally efficient, yet sensitive to complex patterns of association, e.g. interactions, so that informative features are not mistakenly eliminated prior to downstream modeling. This paper focuses on Relief-based algorithms (RBAs), a unique family of filter-style feature selection algorithms that have gained appeal by striking an effective balance between these objectives while flexibly adapting to various data characteristics, e.g. classification vs. regression. First, this work broadly examines types of feature selection and defines RBAs within that context. Next, we introduce the original Relief algorithm and associated concepts, emphasizing the intuition behind how it works, how feature weights generated by the algorithm can be interpreted, and why it is sensitive to feature interactions without evaluating combinations of features. Lastly, we include an expansive review of RBA methodological research beyond Relief and its popular descendant, ReliefF. In particular, we characterize branches of RBA research, and provide comparative summaries of RBA algorithms including contributions, strategies, functionality, time complexity, adaptation to key data characteristics, and software availability.

• The paper reviews Relief-based algorithms and variants, highlighting their evolution and practical application in high-dimensional biomedical data mining.
• The paper explains how algorithms like ReliefF effectively capture feature interactions without exhaustive combinatorial searches.
• The paper demonstrates evaluations on simulated and real-world datasets, paving the way for future improvements in computational efficiency.

Review of "Relief-Based Feature Selection: Introduction and Review"

The paper "Relief-Based Feature Selection: Introduction and Review" by Urbanowicz et al. offers a comprehensive examination of Relief-based algorithms (RBAs) within the domain of feature selection. This work systematically presents the evolution, methodological principles, and practical applications of RBAs, demonstrating their utility and adaptability in various contexts, particularly in high-dimensional biomedical data mining.

Overview of Feature Selection Methods

Feature selection is a critical task in data mining and modeling, primarily concerned with identifying relevant features that influence an endpoint while discarding irrelevant ones. This process significantly impacts the efficiency and accuracy of downstream modeling. The paper categorizes feature selection methods into filter, wrapper, and embedded methods, each with distinct advantages and computational considerations.

1. Filter Methods: These use a proxy measure from training data to score features prior to modeling, thus providing a computational advantage.
2. Wrapper Methods: These involve a modeling algorithm to train and test feature subsets, which can be computationally intensive but potentially more accurate.
3. Embedded Methods: These methods integrate feature selection within the modeling process, optimizing a composite objective function that includes both model fit and feature number penalties.

Relief-Based Algorithms (RBAs)

RBAs stand out within filter methods due to their sensitivity to feature interactions without explicitly evaluating all combinations of features. The original Relief algorithm introduced by Kira and Rendell is an instance-based filter method that iteratively updates feature weights based on nearest neighbor distances, effectively capturing feature dependencies.

Relief Algorithm

The Relief algorithm estimates feature relevance by comparing instances within the feature space and updating weights based on the differences observed between nearest hits (same class) and nearest misses (different class). One of the core strengths of Relief is its ability to detect interactions between features without exhaustive subset evaluations. However, its efficacy diminishes in large feature spaces due to increasing noise and computational complexity.

Key Variants and Extensions

Over the years, several extensions and improvements to the original Relief algorithm have been proposed to address its limitations, enhance its capabilities, and adapt it to various data types and problem settings.

1. ReliefF: This well-known variant introduced multiple nearest neighbors and improved handling of noisy and multi-class data, making it more robust in diverse scenarios.
2. Iterative Relief Methods: Methods such as Iterative Relief and I-RELIEF have been developed to address biases and refine the feature weight estimates through multiple iterations, dynamically adjusting instance distances.
3. Efficiency Enhancements: Algorithms like VLSReliefF and its iterative counterpart, $i$VLSReliefF, focus on improving computational efficiency in large-scale feature spaces by scoring random subsets of features and integrating the results.
4. Interaction Detection: Methods such as SURF, SURF*, and MultiSURF* refine neighbor selection strategies to improve the detection of epistatic interactions, enhancing the algorithms' sensitivity to complex patterns.
5. Handling Various Data Types: Extensions like RReliefF for regression, adaptations for multi-class endpoints, and handling of missing data ensure the applicability of RBAs across a wide range of data types.

Comparative Evaluations and Practical Implications

Extensive evaluations have demonstrated that RBAs, particularly when employing advanced neighbor selection and iterative strategies, outperform traditional feature selection methods in detecting both main effects and interactions. These evaluations have utilized both simulated datasets representing various genetic patterns and real-world biomedical datasets.

Future Directions

Research into RBAs continues to evolve, with potential future developments including:

1. Optimizing instance weighting and neighbor selection to further enhance detection of complex patterns.
2. Scaling RBAs to handle even larger datasets efficiently.
3. Developing adaptive methods that automatically adjust algorithm parameters for diverse problem domains.
4. Expanding the application of RBAs to new data contexts such as temporal data.

Conclusion

Relief-based algorithms represent a powerful family of feature selection techniques capable of handling high-dimensional data while remaining sensitive to intricate interactions among features. The evolution from Relief to advanced variants like ReliefF, Iterative Relief, and MultiSURF underscores a continual improvement in balancing computational efficiency and detection capability. As the field progresses, further methodological advancements and new applications are anticipated, reinforcing the practical utility of RBAs in contemporary data mining challenges.