Granular-Ball Computing (GBC) is an efficient, robust, and highly interpretable multi-granularity representation and computation method. Nonetheless, most feature selection methods based on GBC require considerable time to calculate the significance measures of features or repeatedly generate granular balls, which limits their applicability to high-dimensional data. The graph-based feature selection effectively reduces dimensionality by exploiting feature correlations and redundancies. However, most graph-based feature selection methods are limited to a fine and single granularity knowledge space. Driven by these issues, this paper first proposes the granular-ball divergence-based fuzzy rough set to characterize the uncertain information from a multi-granularity perspective. Then, the minimum discriminative criterion for constructing a hypergraph is evaluated by the approximation operators, and the correlations between theories are established. On this basis, the importance of features is defined as the weights of hypernodes, and the strategies of Important Retaining (IR) and Redundant Pruning (RP) are designed to select the most important feature and improve execution efficiency, respectively, which is equivalent to an iterative weighted maximum coverage problem with dynamic weight updates. Finally, a feature selection algorithm is designed to select the best feature subset. The experimental results show that our algorithm achieves better classification performance and higher execution efficiency.
Ye Li, Lei Yang, Binbin Sang et al.· IEEE Transactions on Knowled...· 1 citation
Index selection is a crucial component in database query optimization. Traditional database index selection is inefficient when handling large-scale and complex structured query language (SQL) queries, and existing methods often overlook index maintenance costs and the necessity of updates. To address these issues, a network-optimized Monte Carlo tree search (NMCTS)-based index selection model, called tree-based index selection (Tree-IS), is proposed by using the sampling-based reinforcement learning algorithm Monte Carlo tree search (MCTS). This model compresses the action space through workload-driven query template extraction and candidate index (CI) generation techniques. It integrates a novel query state representor and an execution-plan-based index value model (IVM), accurately characterizing the database environment while providing reliable action criteria for index search. On this basis, Tree-IS further leverages a state abstraction network (SAN), a policy network, and a value network (VN) to optimize the search logic of MCTS, achieving the rapid identification of optimal index sets and a significant improvement in query efficiency. Extensive experiments are conducted on popular datasets, including join order benchmark (JOB), transaction processing performance council benchmark holistic (TPC-H), and transaction processing performance council benchmark decision support (TPC-DS), to evaluate the proposed method across multiple metrics. The results demonstrate that the quality of indices selected by the Tree-IS model is significantly superior to that of existing methods.
Shaojie Qiao, Lei Yang, Rongmin Tang et al.· IEEE Transactions on Neural...· 0 citations
Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks (GNNs) by selecting important edges to induce subgraphs, where edge importance is assessed by perturbing each edge and observing changes in the model predictions. However, they often neglect the synergistic effects among edges, which are crucial for accurately characterizing edge importance. To address this issue, we propose SeeExplainer, a parameter-free explainer to interpret GNNs. Specifically, we first introduce a granular-ball graph refinement mechanism that decomposes a graph into several disjoint granular-balls with no fixed size, and utilize them as nodes to construct a structural graph. This process can better capture the synergistic effects among edges. Then, we perturb nodes and edges in the structural graph to generate explanatory subgraphs based on their respective contributions. Experiments on several graph classification datasets of different networks show that SeeExplainer outperforms state-of-the-art baselines.
Jian-Cu Chen, Shuyin Xia, Guan Wang et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
