Identifying Simple Shapes to Classify the Big Picture In recent years, Deep Artificial Neural Networks (DNNs) have demonstrated their ability in solving visual classification problems. However, an impediment is transparency where it is difficult to interpret why an object is classified in a particular way. Furthermore, it is also difficult to validate whether a learned model truly represents a problem space. Learning Classifier Systems (LCSs) are an Evolutionary Computation technique capable of producing humanreadable rules that explain why an instance has been classified, i.e. the system is fully transparent. However, because they can encode complex relationships between features, they are not best suited to domains with a large number of input features, e.g. classification in pixel images. Thus, the aim of this work is to develop a novel DNNLCS system where the former extracts features from pixels and the latter classifies objects from these features with clear decision boundaries. Results show that the system can explain its classification decisions on curated image data, e.g. plates have elliptical or rectangular shapes. This work represents a promising step towards explainable artificial intelligence in computer vision.

Broad Learning System for Class Incremental Learning The largescale image datasets such as ImageNet and openended photo websites are revealing new challenges to image classification that were not apparent in smaller and fixed sets. In particular, how to handle the dynamically growing datasets efficiently, where not only the amount of training data but also the number of classes increases over time, remains an unexplored problem. In this challenging setting, we study how to employ the Broad Learning System (BLS) to deal with the incremental increases of sample classes in datasets. We first determine whether a new batch of samples is from certain seen classes or unseen classes by two methods based on the statistics theory. Then, the incremental training algorithms are developed to classify unlearned data from new classes. We test our class incremental learning algorithms of BLS on some representative datasets such as Cifar100. The experimental results show that our methods can accurately distinguish seen and unseen classes. And the comparison study demonstrates that our method can be extended to 100 classes on Cifar100 with an acceptable loss of accuracy and performs better compared to other similar class incremental algorithms such as iCaRL.