Thanks to the exponential growth in computing power and vast amounts of data, artificial intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be ubiquitously adopted in our daily lives. Even though AI-powered systems have brought competitive advantages, the black-box nature makes them lack transparency and prevents them from explaining their decisions. This issue has motivated the introduction of explainable artificial intelligence (XAI), which promotes AI algorithms that can show their internal process and explain how they made decisions. The number of XAI research has increased significantly in recent years, but there lacks a unified and comprehensive review of the latest XAI progress. This review aims to bridge the gap by discovering the critical perspectives of the rapidly growing body of research associated with XAI. After offering the readers a solid XAI background, we analyze and review various XAI methods, which are grouped into (i) pre-modeling explainability, (ii) interpretable model, and (iii) post-modeling explainability. We also pay attention to the current methods that dedicate to interpret and analyze deep learning methods. In addition, we systematically discuss various XAI challenges, such as the trade-off between the performance and the explainability, evaluation methods, security, and policy. Finally, we show the standard approaches that are leveraged to deal with the mentioned challenges.

Explainable artificial intelligence: a comprehensive review

Recently, broad learning system (BLS) has been introduced to solve industrial fault diagnosis problems and has achieved impressive performance. As a flat network, BLS enjoys a simple linear structure, which enables BLS to train and update the model efficiently in an incremental manner, and it potentially has better generalization capacity than deep learning methods when training data are limited. The basic BLS is a supervised learning method that requires all the training data to be labeled. However, in many practical industrial scenarios, data labels are usually difficult to obtain. Existing semisupervised variant uses manifold regularization framework to capture the information of unlabeled data, however, such a method will sacrifice the incremental learning capacity of BLS. Considering that in many practical applications, training data are sequentially generated, in this article, an online semisupervised broad learning system (OSSBLS) is proposed for fault diagnosis in these cases. The proposed method not only can efficiently construct and incrementally update the model, but also can take advantage of unlabeled data to improve the model's diagnostic performance. Experimental results on the Tennessee Eastman process and a real-world air compressor working process demonstrate the superiority of OSSBLS in terms of both diagnostic performance and time consumption.

Online Semisupervised Broad Learning System for Industrial Fault Diagnosis

Time-Series Graph Network for Sea Surface Temperature Prediction

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.

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GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Automatic recognition and classification of microseismic waveforms based on computer vision

Explainable time–frequency convolutional neural network for microseismic waveform classification

Classification over data streams is a crucial task of explosive social stream mining and computing. Efficient learning techniques provide high-quality services in the aspect of content distribution and event browsing. Due to the concept drift and concept evolution in data streams, the classification performance degrades drastically over time. Many existing methods utilize supervised and unsupervised learning strategies. However, supervised strategies require labeled emerging records to update the classifiers, which is unfeasible to work in the practical social stream applications. Although unsupervised strategies are popularly applied to detect concept evolution, it takes tremendous run-time computation cost to run online clustering. To this end, in this paper, we address these major challenges of social stream classification by proposing an efficient incremental semi-supervised classification method named CODES (Classification Over Drifting and Evolving Stream). The proposed CODES method consists of an efficient incremental semi-supervised learning module and a dynamic novelty threshold update module. Thus, in the drifting and evolving social streams, CODES is able to provide: 1) semi-supervised learning ability to eliminate dependency on the labels of emerging records; 2) fast incremental learning with real-time update ability to tackle concept drift; 3) efficient novel class detection ability to tackle concept evolution. Extensive experiments are conducted on several real-world datasets. The results indicate a higher performance than several state-of-the-art methods. CODES achieves efficient learning performance over drifting and evolving social streams, which improves practical significance in the real-world social stream applications.

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CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

An Uncertainty-based Neural Network for Explainable Trajectory Segmentation

Ventricular fibrillation is the most lethal arrhythmia. At present, the treatment of ventricular fibrillation is commonly received after the onset of the disease, which mainly depends on external defibrillation and drug-assisted therapy. Although activity of heartbeats can be described and analyzed using the most popular technique ECG (electrocardiogram), there is still no widely recognized prediction methods for ventricular fibrillation. Therefore, in this paper, in order to realize warning of ventricular fibrillation, we focus on the detection of atrial fibrillation and ventricular flutter, which are the arrhythmias often occurring before ventricular fibrillation. We propose a frequency-domain LSTM (Long Short-Term Memory), which uses heartbeat waves transformed from the original time domain into the frequency domain as input. Furthermore, to address the problem of big ECG data training efficiency and scalability, we also provide an implementation of our method under the distributed computing framework MapReduce in the Spark cluster. Experimental results indicate that our method achieves excellent classification performance compared with rival methods.

Chao Zhang

Papers

Explainable time–frequency convolutional neural network for microseismic waveform classification

CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

An Uncertainty-based Neural Network for Explainable Trajectory Segmentation

Efficient Learning of Big ECG Data for Ventricular Fibrillation Warning