Adaptive Hierarchical Classification Networks

Hierarchical decomposition enables increased number of classes in a classification problem. Class similarities guide the creation of a family of course to fine classifiers which solve categorical problems more effectively than a single flat classifier. High accuracies require precise configurations for each of the family of classifiers. This paper proposes a method to adaptively select the configuration of the hierarchical family of classifiers. Linkage statistics from overall and sub-classification confusion matrices define categorical groupings for an efficient and accurate classification framework. Depending on the number of classes and the complexity of the problem, an adaptive configuration manager chooses between a multi-layer perceptron or a deep convolutional neural network, then selects the complexity of each. Experiments on CalTech101, CalTech256, CIFAR100 and the ImageNet datasets demonstrate performance of adaptive hierarchical models on an image classification task.