Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification.

Manual knowledge acquisition of case retrieval features is expensive and may be infeasible for cases containing hard-to-characterize data such as images. Deep learning (DL) methods excel at extracting useful feature information from raw data, making them appealing for learning feature information. Previous work has demonstrated the promise of integrated systems for case-based image classification, using a deep neural network to generate features which are then used for case retrieval, resulting in classifications that can be explained in terms of prior cases. However, the accuracy of the combined system may lag behind that of the original DL model. In response, our previous work proposed MultiNet, a method using ensembles for localized feature extraction. Multi-Net improved performance, but experiments showed limitations of its design. This paper presents Deep Ensemble Feature Extraction for Retrieval (DEFER), a feature-extraction-based classification approach aimed at addressing those issues. To increase accuracy, DEFER adds a discriminator to focus retrieval within each replica and weighted voting based on confidence in its class prediction, grounded in nearest-neighbor retrieval. In experiments for image classification, DEFER outperforms analogous DL-only and DL-case-based systems, supporting that its approach can improve performance.