Automatic multi-objective based feature selection for classification

TL;DR

This paper introduces a multi-objective feature selection algorithm that optimizes sensitivity and specificity for classifying lesion malignancy, improving radiomic-based diagnostic accuracy.

Contribution

It proposes a novel multi-objective feature selection method with automatic termination, solution selection, and mutation adaptation, enhancing radiomic feature selection for classification tasks.

Findings

MO-FS outperforms traditional methods in classification accuracy.

The method effectively balances sensitivity and specificity.

It is applicable to lung and breast lesion malignancy classification.

Abstract

Objective: Accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical. Methods: This work proposes a new multi-objective based feature selection (MO-FS) algorithm that considers sensitivity and specificity simultaneously as the objective functions during feature selection. For MO-FS, we developed a modified entropy based termination criterion (METC) that stops the algorithm automatically rather than relying on a preset number of generations. We also designed a solution selection methodology for multi-objective learning that uses the evidential reasoning approach (SMOLER) to automatically select the optimal solution from the Pareto-optimal set. Furthermore, we developed an adaptive mutation operation to generate the mutation probability in MO-FS automatically. Results: We evaluated the MO-FS for classifying lung nodule malignancy in low-dose CT and breast lesion malignancy in digital breast tomosynthesis. Conclusion: The experimental results demonstrated that the feature set selected by MO-FS achieved better classification performance than features selected by other commonly used methods. Significance: The proposed method is general and more effective radiomic feature selection strategy.