Instance weighting versus threshold adjusting for cost-sensitive classification

In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive learning methods that attempt to minimize average misclassification cost rather than plain error rate. Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two approaches on several standard, off-the-shelf classification methods. The comparison indicates that the two approaches lead to similar results for some classification methods, such as Naïve Bayes, logistic regression, and backpropagation neural network, but very different results for other methods, such as decision tree, decision table, and decision rule learners. The findings from this research have important implications on the selection of the cost-sensitive classifier learning approach as well as on the interpretation of a recently published finding about the relative performance of Naïve Bayes and decision trees.     

Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series

A dynamic Bayesian network (DBN) is one of popular approaches for relational knowledge discovery such as modeling relations or dependencies, which change over time, between variables of a dynamic system. In this paper, we propose an adaptive learning method (autoDBN) to learn DBNs with changing structures from multivariate time series. In autoDBN, segmentation of time series is achieved first through detecting geometric structures transformed from time series, and then model regions are found from the segmentation by designed finding strategies; in each found model region, a DBN model is established by existing structure learning methods; finally, model revisiting is developed to refine model regions and improve DBN models. These techniques provide a special mechanism to find accurate model regions and discover a sequence of DBNs with changing structures, which are adaptive to changing relations between multivariate time series. Experimental results on simulated and real time series show that autoDBN is very effective in finding accurate/reasonable model regions and gives lower error rates, outperforming the switching linear dynamic system method and moving window method.

Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.     

先验知识与基于核函数的回归方法的融合

In some sample based regression tasks, the observed samples are quite few or not informative enough. As a result, the conflict between the number of samples and the model complexity emerges, and the regression method will confront the dilemma whether to choose a complex model or not. Incorporating the prior knowledge is a potential solution for this dilemma. In this paper, a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed. The proposed prior knowledge based kernel regression (PKBKR) method includes two subproblems: representing the prior knowledge in the function space, and combining this representation and the training samples to obtain the regression function. A greedy algorithm for the representing step and a weighted loss function for the incorporation step are proposed. Finally, experiments are performed to validate the proposed PKBKR method, wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity, especially when the number of samples is small or the observation noise is large.     

基于集成的年龄估计方法

近十年来, 由于广泛的应用前景, 关于人脸识别的研究得到了广泛的关注. 但目前有一种影响人脸识别技术的因素尚未被研究者所重视, 那就是年龄变化. 而在适用于年龄变化的人脸识别技术中有一个重要的问题, 即年龄估计. 本文基于典型相关分析和代价敏感学习提出了两种年龄估计算法, 并在此基础上利用集成技术来提高年龄估计的准确性. 最终实验结果验证了本文方法的有效性.     

MINI——一种可减小变更影响范围的本体演化算法

本体演化会影响依赖本体的服务,使其重新修订和重新部署.面对同一变更需求,不同演化实现方法造成的影响范围差别很大.当前的本体演化研究主要集中在如何实现变更需求以及维护变更前后本体的一致性,对于如何降低演化影响范围关注甚少.文中提出了一种可以有效减小变更影响范围的本体演化算法MINI.该算法首先分析了本体实体和服务之间的依赖关系并提出了量化变更影响范围的数学公式.根据这一公式,MINI算法将本体演化过程转变为图的启发式搜索过程,通过搜索一条影响值最小的变更路径来减小本体演化的影响范围.实验结果表明,MINI算法导致的平均变更影响范围大大小于现有的本体演化算法.该算法已在某实际项目中得以应用和验证.     

Informal learning with PDAs and smartphones

Abstract There has been increasing interest in informal learning in recent years alongside interest in how such learning can be supported by technology. However, relatively little is known about the extent to which adults make use of their own mobile devices to support informal learning. In this study, a survey was used to investigate whether, and to what extent, experienced users of mobile devices use their mobile devices to support intentional informal learning. If so, do they make use of mobile device connectivity to support opportunistic informal learning and does such connectivity support or encourage collaborative informal learning? Experienced mobile device users were recruited from web forums and business, and asked whether they used their devices to support informal learning. A pattern of learning uses emerged, some of which deployed the mobile device capabilities relatively unchanged, others triggered adaptations to typical learning activities to provide a better fit to the needs of the learner. These informal learning activities provided the basis for the design of a flexible mobile learning framework that can be extended to support developments in mobile technology, and increasing use of Web 2.0 technologies by informal learners.     

Technology-assisted learning: a longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning

Abstract   A field experiment compares the effectiveness and satisfaction associated with technology-assisted learning with that of face-to-face learning. The empirical evidence suggests that technology-assisted learning effectiveness depends on the target knowledge category. Building on Kolb's experiential learning model, we show that technology-assisted learning improves students' acquisition of knowledge that demands abstract conceptualization and reflective observation but adversely affects their ability to obtain knowledge that requires concrete experience. Technology-assisted learning better supports vocabulary learning than face-to-face learning but is comparatively less effective in developing listening comprehension skills. In addition, according to empirical tests, perceived ease of learning and learning community support significantly predict both perceived learning effectiveness and learning satisfaction. Overall, the results support our hypotheses and research model and suggest instructors should consider the target knowledge when considering technology-assisted learning options or designing a Web-based course. In addition, a supportive learning community can make technology-assisted learning easier for students and increase their learning satisfaction.     

Application of reinforcement learning to the game of Othello

Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such complex problems are associated with some difficulties. As we discuss in this article, these methods are plagued by the so-called curse of dimensionality and the curse of modelling. In this article, we discuss reinforcement learning, a machine learning technique for solving sequential decision making problems with large state spaces. We describe how reinforcement learning can be combined with a function approximation method to avoid both the curse of dimensionality and the curse of modelling. To illustrate the usefulness of this approach, we apply it to a problem with a huge state space—learning to play the game of Othello. We describe experiments in which reinforcement learning agents learn to play the game of Othello without the use of any knowledge provided by human experts. It turns out that the reinforcement learning agents learn to play the game of Othello better than players that use basic strategies.