Breast Cancer Prediction with Hybrid Filter-Wrapper Feature Selection
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Keywords:
Feature Selection, Filter Methods, Wrapper Methods, Hybrid Feature Selection, Hybrid Filter-WrapperAbstract
Feature selection, the process of selecting a subset of relevant features for model construction,
plays a pivotal role in machine learning tasks, particularly in enhancing model efficiency and performance.
It aids in mitigating the curse of dimensionality, reducing computational costs, and improving the
generalization of models. Among the various methods employed in feature selection, both filter and
wrapper methods stand out for their effectiveness. However, the integration of hybrid versions of these
methods holds promising prospects in further enhancing model performance. In a recent study utilizing a
breast cancer dataset, encompassing 30 features, the utilization of traditional methods yielded an ROC AUC
score of 0.943. Upon employing the hybrid feature selection technique proposed herein, the ROC AUC
score surged to 0.954 after selecting a reduced set of 10 features. This significant improvement underscores
the efficacy of the proposed method in enhancing model performance, thus affirming its superiority in
optimizing predictive accuracy and robustness.
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