Traditional machine learning models provide a single-class prediction for a given input instance. This may be inadequate in some scenarios, especially when the cost of erroneous predictions is high. Cautious random forests are cautious classification models that may output sets of possible classes as predictions when uncertainty is high, thus reducing the risk of making incorrect decisions. However, making such indeterminate predictions carries a cost, as resolving indeterminacy typically necessitates further analysis and manual intervention. This work focuses on explaining why an indeterminate prediction has been made and how indeterminacy can be resolved. To this end, we use counterfactual examples associated with determinate predictions. We propose a branch-and-bound algorithm that can efficiently generate proximal, plausible, and actionable counterfactual examples. Several experimental results are presented to demonstrate the advantages of our proposed method.

In order to alleviate urban congestion, improve vehicle mobility, and improve logistics delivery efficiency, this paper establishes a practical multi-objective and multi constraint logistics delivery mathematical model based on graphs, and proposes a solution algorithm framework that combines decomposition strategy and deep reinforcement learning (DRL). Firstly, taking into account the actual multiple constraints such as customer distribution, vehicle load constraints, and time windows in urban logistics distribution regions, a multi constraint and multi-objective urban logistics distribution mathematical model was established with the goal of minimizing the total length, cost, and maximum makespan of urban logistics distribution paths. Secondly, based on the decomposition strategy, a DRL framework for optimizing urban logistics delivery paths based on Graph Capsule Network (G-Caps Net) was designed. This framework takes the node information of VRP as input in the form of a 2D graph, modifies the graph attention capsule network by considering multi-layer features, edge information, and residual connections between layers in the graph structure, and replaces probability calculation with the module length of the capsule vector as output. Then, the baseline REINFORCE algorithm with rollout is used for network training, and a 2-opt local search strategy and sampling search strategy are used to improve the quality of the solution. Finally, the performance of the proposed method was evaluated on standard examples of problems of different scales. The experimental results showed that the constructed model and solution framework can improve logistics delivery efficiency. This method achieved the best comprehensive performance, surpassing the most advanced distress methods, and has great potential in practical engineering.

International audience

Abstract In this paper, we present a formal framework to (1) aggregate probabilistic ensemble members into either a representative classifier or a credal classifier, and (2) perform various decision tasks based on this uncertainty quantification. We first elaborate on the aggregation problem under a class of distances between distributions. We then propose generic methods to robustify uncertainty quantification and decisions, based on the obtained ensemble and representative probability. To facilitate the scalability of the proposed framework, for all the problems and applications covered, we elaborate on their computational complexities from the theoretical aspects and leverage theoretical results to derive efficient algorithmic solutions. Finally, relevant sets of experiments are conducted to assess the usefulness of the proposed framework in uncertainty sampling, classification with a reject option, and set-valued prediction-making.

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

Abstract Target detection in autonomous driving tasks presents a complex and critical challenge due to the diversity of targets and the intricacy of the environment. To address this issue, this paper proposes an enhanced YOLOv8 model. Firstly, the original large target detection head is removed and replaced with a detection head tailored for small targets and high‐level semantic details. Secondly, an adaptive feature fusion method is proposed, where input feature maps are processed using dilated convolutions with different dilation rates, followed by adaptive feature fusion to generate adaptive weights. Finally, an improved attention mechanism is incorporated to enhance the model's focus on target regions. Additionally, the impact of Group Shuffle Convolution (GSConv) on the model's detection speed is investigated. Validated on two public datasets, the model achieves a mean Average Precision (mAP) of 53.7% and 53.5%. Although introducing GSConv results in a slight decrease in mAP, it significantly improves frames per second. These findings underscore the effectiveness of the proposed model in autonomous driving tasks.

To address the issue of low accuracy in speech fatigue recognition (SFR) under small samples, a method for small-sample SFR based on generative adversarial networks (GANs) is proposed. First, we enable the generator and discriminator to adversarially train and learn the features of the samples, and use the generator to generate high-quality simulated samples to expand our dataset. Then, we transfer discriminator parameters to fatigue identification network to accelerate network training speed. Furthermore, we use a bidirectional long short-term memory network (BLSTM) to further learn temporal fatigue features and improve the recognition rate of fatigue. 720 speech samples from a self-made Chinese speech database (SUSP-SFD) were chosen for training and testing. The results indicate that compared with traditional SFR methods, like convolutional neural networks (CNNs) and long short-term memory network (LSTM), our method improved the SFR rate by about 2.3–6.7%, verifying the effectiveness of the method.

Automatic and accurate segmentation of tumor area from rectal CT image plays an extremely key role in the treatment and diagnosis of rectal cancer. This paper proposes the MR-U-Net network model. The improvement is that a pair of encoder and decoder is added longitudinally to the U-shaped structure, which is the network structure of the fifth layer, and a residual module is added horizontally to the encoder and decoder of each layer. This model is used to conduct targeted research on the automatic segmentation method of rectal cancer. [H. Gao et al., Rectal tumor segmentation method based on U-Net improved model, J. Comput. Appl.40(8) (2020) 2392–2397] also improved U-Net and used the same dataset as this paper, but the Dice coefficient of all targets was only 83.15%, and the Dice coefficient of small targets was only 87.17%. This paper evaluates the improved MR-U-Net network model with the three indicators of precision, recall and Dice coefficient, and finds that in comparison to Ref. 4 the precision is 95.13%, 2.29% higher than the former work, recall is 94.28%, higher than the former work by 0.34%, Dice coefficient of all targets is 88.45%, increased by 5.3% compared with the former work, and the small targets Dice coefficient is increased by 1.28%, which is the best optimization state of this paper. Experiments show that for datasets with extremely skewed positive and negative samples, the MR-U-Net network structure after improving the hyperparameters in the optimizer can more accurately segment the rectal CT tumor lesion area.