This paper presents two complementary enhancements to the Very Fast Decision Tree algorithm for data stream mining. The first contribution introduces the fractional Hoeffding bound, a relaxed splitting criterion where the original threshold is scaled by a factor . Experimental evidence shows that for this modification, the resulting trees not only grow faster but also achieve higher accuracy compared to the original Very Fast Decision tree. The second contribution proposes a novel data structure, called extended statistics, which extends the traditional sufficient statistics used in the Very Fast Decision Tree by maintaining additional information about attribute co-occurrences. This allows child nodes to inherit richer knowledge from their parent, leading to deeper trees with accelerated convergence to the target accuracy. Numerical experiments on synthetic data streams demonstrate that the combination of fractional bounds and multi-index statistics yields significant accuracy gains, particularly in the early phases of learning. The extended statistics improvement, however, comes at the cost of increased memory and computational requirements, emphasizing the trade-off between predictive performance and resource usage.

Computing generates intelligence. With this statement we do not mean computing’s capabilities of manipulating numbers, shapes, symbols, and even logics. What we mean is the ingenious design of computing structures which serve as the basis of intelligence generation during program running. In this panel discussion, we consider how to obtain such capabilities through some computing paradigms as examples, including principal computing, logic computing, discriminative computing, and generative computing. The panelists express their thoughts about the inherent advantages and disadvantages of each of these paradigms, in terms of their adaptivity, interpretability, generality and specificity, and dives into detailed discussions about Large Language Models (LLMs), a mainstream generative paradigm which leverages the strengths of large pre-trained models and downstream prompt tuning to deliver combined intelligence, superior to most existing frameworks in natural language processing. The panel outlines potential challenges of the generative paradigm, with a strong focus on LLMs, and emphasizes that future directions of such models will need to address (1) tackling bias, discrimination, and transparency challenges; (2) delivering logical answers with high specificity; (3) enabling personalized, lightweight, and rapid updating mechanisms; (4) assessing accreditation, tracing, and misusages; and (5) ensuring sustainable LLMs.

Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified (e.g., in the Hoeffding tree algorithm) or time-consuming (e.g., in the McDiarmid tree algorithm). In this paper, we propose a new method which significantly outperforms the McDiarmid tree algorithm and has a solid mathematical basis. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.

In mining data streams the most popular tool is the Hoeffding tree algorithm. It uses the Hoeffding's bound to determine the smallest number of examples needed at a node to select a splitting attribute. In the literature the same Hoeffding's bound was used for any evaluation function (heuristic measure), e.g., information gain or Gini index. In this paper, it is shown that the Hoeffding's inequality is not appropriate to solve the underlying problem. We prove two theorems presenting the McDiarmid's bound for both the information gain, used in ID3 algorithm, and for Gini index, used in Classification and Regression Trees (CART) algorithm. The results of the paper guarantee that a decision tree learning system, applied to data streams and based on the McDiarmid's bound, has the property that its output is nearly identical to that of a conventional learner. The results of the paper have a great impact on the state of the art of mining data streams and various developed so far methods and algorithms should be reconsidered.

Takagi-Sugeno fuzzy systems are very common learning systems. The paper is about building classification ensembles from them and merging resulting rule bases. When merged, the rule base is more intelligible and easier to process. The merging is possible thanks to a modification of TS systems. Numerical simulations show that the modified systems perform very well