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The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model's robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN. • Inductive learning on partially labeled attributed graphs is addressed. • An adversarially regularized graph attention model is proposed. • Experiments on real-world datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Abstract: There are a lot of systems that make decisions or classifications on the basis of a number of rules. This set of rules that govern such a system is called the rule base. When a new system of this kind is being developed, setting up its rule base is a time-consuming and expensive process because the rule base contains the knowledge of the outside world, which could be acquired from experts or produced from previous experiences. In this latter case, machine-learning algorithms can help. In fact, many methods have been proposed to generate rules from training instances. The aim of this paper is to present a new fuzzy learning algorithm to generate IF-THEN rules, for classifying instances in one application domain. This algorithm can improve the results offered by a previously presented algorithm. In addition, the more common classification problems of the original algorithm are presented and a measure to determine the conflicts among generated rules is introduced. Moreover, we study the classification stage of that inductive fuzzy learning algorithm and an improvement is suggested to obtain better classification results. [Copyright &y& Elsevier]

Abstract: This paper presents Feature Interval Learning algorithms (FIL) which represent multi-concept descriptions in the form of disjoint feature intervals. The FIL algorithms are batch supervised inductive learning algorithms and use feature projections of the training instances to represent induced classification knowledge. The concept description is learned separately for each feature and is in the form of a set of disjoint intervals. The class of an unseen instance is determined by the weighted-majority voting of the feature predictions. The basic FIL algorithm is enhanced with adaptive interval and feature weight schemes in order to handle noisy and irrelevant features. The algorithms are empirically evaluated on twelve data sets from the UCI repository and are compared with k-NN, k-NNFP, and NBC classification algorithms. The experiments demonstrate that the FIL algorithms are robust to irrelevant features and missing feature values, achieve accuracy comparable to the best of the existing algorithms with significantly less average running times. [Copyright &y& Elsevier]

Abstract: Extant multiple-cost-sensitive learning algorithms are usually designed for dealing with misclassification cost (MC) and test cost (TC) together. This paper outlines a new learning algorithm, called cost-time sensitive classification, designed for minimizing tangible costs (which includes TC and waiting cost (WC)) as well as maximizing the decrease of the intangible costs (also called MC). The proposed algorithm induces decision trees from training datasets with missing data, in which the costs are measured in different units. Firstly, a split criterion is proposed for building cost-time sensitive decision trees, aiming at possibly reducing the intangible cost. Then a hybrid test strategy, which can handle missing values in test datasets, is designed for combining the sequential test with the batch test strategy. To evaluate the efficiency of the proposed method, extensive experiments were conducted on the UCI datasets at different missing rates. The experimental results show that the proposed algorithm achieves better than the existing ones in terms of reducing the intangible costs when taking into account waiting costs. [Copyright &y& Elsevier]

Abstract: Over a decade ago, Friedman et al. introduced the Tree Augmented Naïve Bayes (TAN) classifier, with experiments indicating that it significantly outperformed Naïve Bayes (NB) in terms of classification accuracy, whereas general Bayesian network (GBN) classifiers performed no better than NB. This paper challenges those claims, using a careful experimental analysis to show that GBN classifiers significantly outperform NB on datasets analyzed, and are comparable to TAN performance. It is found that the poor performance reported by Friedman et al. are not attributable to the GBN per se, but rather to their use of simple empirical frequencies to estimate GBN parameters, whereas basic parameter smoothing (used in their TAN analyses but not their GBN analyses) improves GBN performance significantly. It is concluded that, while GBN classifiers may have some limitations, they deserve greater attention, particularly in domains where insight into classification decisions, as well as good accuracy, is required. [Copyright &y& Elsevier]

Abstract: The objective of this study is to present a new algorithm, REX-1, developed for automatic knowledge acquisition in Inductive Learning. It aims at eliminating the pitfalls and disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. Using some widely used set of examples such as IRIS, Balance and Balloons, Monk, Splice, Promoter, Lenses, Zoo, and Vote, our algorithm is compared with other well-known algorithms such as ID3, C4.5, ILA, and Rules Family. [Copyright &y& Elsevier]

Dynamic network representation has aroused increasing attention from researchers since it reflects the characteristics of real-world networks and has been widely used in various applications. Enormous efforts have been made to this problem. Existing studies often use fixed-time steps. However, in fact, the dynamics in real-world networks are usually irregular over time. To address this problem, in this paper, we employ ordinary differential equations and propose a novel graph representation approach for dynamic networks, namely Graph-ODE. The core idea lies in that it leverages ordinary differential equations to model the continuous latent dynamics of the network and therefore can naturally capture the evolution pattern. Specifically, to deal with the irregularity of node interactions, we first propose a differential form of GRU aggregator to aggregate the chronological neighbors. In addition, we employ neighbor sampling strategies for efficiency. Besides, our inductive algorithm owns the ability to deal with new emerging nodes. Experimental results on various real-world data sets demonstrate that our method significantly outperforms several state-of-the-art algorithms on downstream tasks. [ABSTRACT FROM AUTHOR]