To maximize the benefit that can be derived from the information implicit in big data, ensemble methods generate multiple models with sufficient diversity through randomization or perturbation. A k-dependence Bayesian classifier (KDB) is a highly scalable learning algorithm with excellent time and space complexity, along with high expressivity. This paper introduces a new ensemble approach of KDBs, a k-dependence forest (KDF), which induces a specific attribute order and conditional dependencies between attributes for each subclassifier. We demonstrate that these subclassifiers are diverse and complementary. Our extensive experimental evaluation on 40 datasets reveals that this ensemble method achieves better classification performance than state-of-the-art out-of-core ensemble learners such as the AODE (averaged one-dependence estimator) and averaged tree-augmented naive Bayes (ATAN).

Due to the excellent classification performance and expressivity, the study of Bayesian network classifiers (BNCs) has attracted great attention ever since the success of Naive Bayes (NB). Information theory has established mathematical basis for the rapid development of BNC. In this paper we propose the definition of entropy function HB(D), which corresponds to the optimal number of bits encoded in the network structure B and can roughly measure the amount of information implicated in training data D. Each factor in HB(D) explicitly represents statements about causal relationships. An efficient heuristic search strategy is introduced to minimize HB(D) and explore the optimal topology of BNC. Our extensive experimental evaluation on 40 datasets reveals that this out-of-core algorithm achieves competitive classification performance compared to state-of-the-art learners such as tree augmented Naive Bayes, k-dependence Bayesian classifier, support vector machine, logistic regression and neural network.

The cooperation in mixed cooperative-competitive tasks has drawn significant attention in multi-agent deep reinforcement learning. Agents need to cooperate with their teammates while competing against their opponents. However, most existing works treat the cooperative agents and competitive agents equally as they perform the same operation on all the agents. As a result, without distinguishing between cooperative and competitive agents, they may suffer from information disorder in learning an optimally cooperative policy and struggle to decide on the next step action. To address the above issues, we decompose the final Q-value into a weighted combination of three parts: the Q-values of the cooperative agents, the competitive group, and the current agent. A theoretical proof of the correctness of the decomposition is provided. With this decomposition, we are able to consider cooperative and competitive agents separately. Accordingly, we propose a multi-agent actor-critic algorithm called actor-double-attention-critic (ADAC) under centralized training and decentralized execution according to the decomposition. In ADAC, networks with group-specific attention and an attentional weighting network are specially designed. With the designed double-attention structure, ADAC can capture the distributions from different agents and improve cooperation performance. Extensive experiments are conducted in three scenarios with nine settings against six representative methods. The results demonstrate the superiority of the proposed ADAC model against state-of-the-art methods in various mixed cooperative-competitive tasks. The code is available at https://github.com/CrazyBayes/ADAC

Bayesian networks have long been a popular medium for graphically representing the probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive Bayes (TAN) builds maximum weighted spanning tree to represent 1-dependence relationships between attributes. In this paper, we propose to optimize the structure of TAN applying heuristic search to sort attribute and filtering technique to remove weak conditional dependencies. Extensive experimental results on 35 data sets from University of California at Irvine (UCI) machine learning repository reveal that the proposed algorithm achieves competitive generalization performance and even outperforms higher-dependence BNCs like k-dependence Bayesian network while retaining excellent strucutre complexity.

The knowledge concept recommendation in Massive Open Online Courses (MOOCs) is a significant issue that has garnered widespread attention. Existing methods primarily rely on the explicit relations between users and knowledge concepts on the MOOC platforms for recommendation. However, there are numerous implicit relations (e.g., shared interests or same knowledge levels between users) generated within the users' learning activities on the MOOC platforms. Existing methods fail to consider these implicit relations, and these relations themselves are difficult to learn and represent, causing poor performance in knowledge concept recommendation and an inability to meet users' personalized needs. To address this issue, we propose a novel framework based on contrastive learning, which can represent and balance the explicit and implicit relations for knowledge concept recommendation in MOOCs (CL-KCRec). Specifically, we first construct a MOOCs heterogeneous information network (HIN) by modeling the data from the MOOC platforms. Then, we utilize a relation-updated graph convolutional network and stacked multi-channel graph neural network to represent the explicit and implicit relations in the HIN, respectively. Considering that the quantity of explicit relations is relatively fewer compared to implicit relations in MOOCs, we propose a contrastive learning with prototypical graph to enhance the representations of both relations to capture their fruitful inherent relational knowledge, which can guide the propagation of students' preferences within the HIN. Based on these enhanced representations, to ensure the balanced contribution of both towards the final recommendation, we propose a dual-head attention mechanism for balanced fusion. Experimental results demonstrate that CL-KCRec outperforms several state-of-the-art baselines on real-world datasets in terms of HR, NDCG and MRR.