Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

TL;DR: Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning (MIL) improves image classification performance by selecting relevant features based on their correlation with the class label.

Abstract: Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification.