An in-depth and contrasting survey of meta-heuristic approaches with classical feature selection techniques specific to cervical cancer.

Data mining and machine learning algorithms' performance is degraded by data of high-dimensional nature due to an issue called "curse of dimensionality". Feature selection is a hot research topic where a subset of features are selected to reduce the dimensionality and thereby increasing the accuracy of learning algorithms. Redundant and irrelevant features are eliminated. Cervical cancer is most commonly occurring disease and driving reasons for untimely mortality among ladies worldwide especially in emerging low income nations like India. However, literature shows that the early identification and exact conclusion of cervical malignant growth can increase the survival chances. The disease does not show signs of its presence in the early stages of its growth. Automated classification and diagnosis of cervical cancer using machine learning and deep learning techniques is in high demand as it allows timely, accurate and regular study of the patient's health progress. Meta-heuristics algorithms provide a global problem independent optimal solution and applied on feature selection problem since decades. In spite of having a good number of literature, there is no survey on feature selection techniques applied to cervical cancer dataset. This paper presents a brief survey on meta-heuristic, its variants, hybrid meta-heuristic and hyper-heuristic techniques. This survey summarizes the feature selection techniques applied to the cervical cancer data to identify the research gap thereby guiding the researchers in the future research direction. The summary of categorization of the techniques such as nature-inspired or non-nature inspired and trajectory or population based is also highlighted. The survey provides a comparative literature review involving classical feature selection techniques and feature selection using metaheuristic algorithms for cervical cancer classification application. [ABSTRACT FROM AUTHOR]