Multi-label classification by exploiting label correlations

Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called multi-label classification using rough sets (MLRS) and MLRS using local correlation (MLRS-LC). The proposed algorithms consider two important factors that affect the accuracy of prediction, namely the correlation among the labels and the uncertainty that exists within the mapping between the feature space and the label space. MLRS provides a global view at the label correlation while MLRS-LC deals with the label correlation at the local level. Given a new instance, MLRS determines its location and then computes the probabilities of labels according to its location. The MLRS-LC first finds out its topic and then the probabilities of new instance belonging to each class is calculated in related topic. A series of experiments reported for seven multi-label datasets show that MLRS and MLRS-LC achieve promising performance when compared with some well-known multi-label learning algorithms.     

非确定规划及带有时间和资源的规划的研究*

智能规划是人工智能近年来的研究热点, 早期的工作主要是围绕着具有较强约束的经典规划展开, 最近的工作放宽了这些假设, 使智能规划逐渐走向应用。在分析经典规划特点的基础上, 介绍了非确定规划的研究进展和带有时间和资源的规划的研究, 并对智能规划的进一步工作和存在的问题提出了一些看法。     

人工智能知识体系及学科综述

本文以人工智能的知识体系为研究内容,阐述人工智能的分支及其分类,以人工智能的知识单元为组织基础,总结与知识单元相关的学科、理论基础、代表性成果及方法,描述知识单元之间的层次关系,指出人工智能目前的重要研究问题。     

Ensemble methods for multi-label classification

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms.     

Applying semi-supervised learning in hierarchical multi-label classification

In classification problems with hierarchical structures of labels, the target function must assign labels that are hierarchically organized and it can be used either for single-label (one label per instance) or multi-label classification problems (more than one label per instance). In parallel to these developments, the idea of semi-supervised learning has emerged as a solution to the problems found in a standard supervised learning procedure (used in most classification algorithms). It combines labelled and unlabelled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label hierarchical classification. Therefore, this paper proposes a new method for supervised hierarchical multi-label classification, called HMC-RAkEL. Additionally, we propose the use of semi-supervised learning, self-training, in hierarchical multi-label classification, leading to three new methods, called HMC-SSBR, HMC-SSLP and HMC-SSRAkEL. In order to validate the feasibility of these methods, an empirical analysis will be conducted, comparing the proposed methods with their corresponding supervised versions. The main aim of this analysis is to observe whether the semi-supervised methods proposed in this paper have similar performance of the corresponding supervised versions.     

Learning multi-label scene classification

In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classification, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a field scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a different treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classification; furthermore, our work appears to generalize to other classification problems of the same nature.     

Multilabel classification via calibrated label ranking

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. The key idea of the approach is to introduce an artificial calibration label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed as a combination of pairwise preference learning and the conventional relevance classification technique, where a separate classifier is trained to predict whether a label is relevant or not. Empirical results in the area of text categorization, image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.     

环境智能中上下文推理方法研究综述

上下文推理是环境智能研究的核心问题之一,与环境智能系统的觉察、响应及适应能力紧密相关,近年来受到国内外研究者的广泛关注。文中分析介绍上下文推理的主要研究内容、研究方法和研究进展,并探讨目前存在的问题及未来的发展方向。     

人工智能的挑战

人工智能（Artificial Intelligence)作为当前科学技术发展中的一门前沿学科,面临很多争论、困难和挑战,本文从两大方面论述了人工智能面临的机遇和挑战。     

人工智能综述

人工智能是研究使计算机来模拟人的某些思维过程和智能行为的学科,主要包括计算机实现的智能的原理、制造类似于人脑智能的计算机,使计算机能实现更高层次的应用。     

What's in a symbol: ontology,representation and language

This paper is in a form unconventional in modern journals but traditional for the discussion of foundational questions: a dialogue. It is a form that makes it possible to contrast two deeply held but incompatible views, each with its standard forms of defence, in order to seek common ground and make the differences more precise. In artificial intelligence, or at least in the major part of it still committed to symbolic representations, there is a long history of discussion of the origin and nature of the symbols we use in representations, symbols which normally look like words, English words in fact, but which most researchers deny are such words, since to concede that would put in question the abstract nature of the representation. In what follows, we examine our common ground and then diverge over five specific questions on the issue of representations. The discussion focuses on symbol use in representations of language, because there the similarity is most acute—between the representation and the represented—but the issues are general and apply to symbolic AI as such.     

Dependent binary relevance models for multi-label classification

Several meta-learning techniques for multi-label classification (MLC), such as chaining and stacking, have already been proposed in the literature, mostly aimed at improving predictive accuracy through the exploitation of label dependencies. In this paper, we propose another technique of that kind, called dependent binary relevance (DBR) learning. DBR combines properties of both, chaining and stacking. We provide a careful analysis of the relationship between these and other techniques, specifically focusing on the underlying dependency structure and the type of training data used for model construction. Moreover, we offer an extensive empirical evaluation, in which we compare different techniques on MLC benchmark data. Our experiments provide evidence for the good performance of DBR in terms of several evaluation measures that are commonly used in MLC.     

人工鱼的认知建模方法研究

“人工鱼”利用人工生命方法创作计算机动画。为了进一步提高动画角色的智能水平,将人工智能方法学引入到“人工鱼”系统中,建立认知模型,控制人工动物的行为,使人工动物成为更加自主的和智能的角色。该文介绍了认知建模的概念、特点,给出人工鱼的认知建模方法研究的主要内容和模型结构。     

面向TRIZ理论使用者的多标签专利分类

专利是创新的结果,更是再创造的知识源泉,对专利技术知识依据创新需求的分类可有效帮助设计者进行创新设计。依据TRIZ理论对产品专利进行自动分类,以辅助利用专利蕴含的技术冲突进行产品创新设计。TRIZ原始的发明原理过于抽象以及有些原理之间有重叠,文中对40个原始的发明原理进行重组,形成20个新的类别。专利自动分类是一类典型的多标签分类问题,文中从Pro_Techniques和CREAX两个软件中收集了针对发明原理进行具体解释的专利数据,并依据此数据集对问题转换和自适应算法两类多标签分类算法进行对比分析。采用海明损失、测度等评估特性评估了上述算法的性能和质量。结果表明,在使用TRIZ专利数据集时,问题转换方法分类性能要明显优于自适应算法。     

基于超椭球支持向量机的兼类文本分类算法

提出一种基于超椭球支持向量机的多类文本分类算法。对每一类样本,利用超椭球支持向量机方法在特征空间求得一个超椭球,使其包含该类尽可能多的样本,同时将噪音点排除在外。分类时,利用待分类样本映射到每个超椭球球心的马氏距离确定其类别。在标准数据集Reuters 21578上的实验结果表明,该算法有效地提高了分类精度。     

浅谈信息时代人工智能的应用与发展

21世纪是计算机科技飞速发展的时代,随着科技的不断发展,一些新型人工智能技术正在走进人类的生活,其中特别值得我们注意的是智能体技术。通过这篇文章我们要了解人工智能的几种类型和应用以及在未来的发展,并对人工智能的发展前景进行分析。     

师范院校计算机人工智能双语教学的方案设计

双语教学作为一个新的教学模式在高校的教学中还处于探索、研究阶段。本文结合人工智能课程的双语教学实践,详细分析了师范院校双语教学的特点,提出了师范院校双语教学遵循的原则,最后设计出了人工智能双语教学的方案。