基于标签特征和相关性的多标签分类算法

针对标签特有特征和标签相关性的有效利用，提出了一种新的多标签算法LSFLC，它可以有效地集成标签特有特征和标签相关性。首先，对于每个标签，通过重采样技术生成新的正类实例以扩充其正类实例的数目；其次，通过特征映射函数将原始特征空间转换为特定的特征空间，得到每个标签的标签特征集；然后，对于每个标签，找到与其最相关标签，通过复制该标签的正类实例来扩大标签特征集，这不仅丰富了标签的信息，而且在一定程度上改善了类不平衡的问题；最后，对于不同的数据集进行实验分析，实验结果表明该算法的分类效果更好。     

基于径向基函数的多目标回归特征构建算法

多目标回归（MTR）是一种针对单个样本同时具有多个连续型输出的回归问题。现有的多目标回归算法都基于同一个特征空间学习回归模型,而忽略了各输出目标本身的特殊性质。针对这一问题,提出基于径向基函数的多目标回归特征构建算法。首先,将各目标的输出作为额外的特征对各输出目标进行聚类,根据聚类中心在原始特征空间构成了目标特定特征空间的基;然后,通过径向基函数将原始特征空间映射到目标特定特征空间,构造目标特定的特征,并基于这些目标特定特征构建各输出目标的基回归模型;最后,用基回归模型的输出组成隐藏空间,采用低秩学习算法在其中发掘和利用输出目标之间的关联。在18个多目标回归数据集上进行实验,并把所提算法与层叠单目标回归（SST）、回归器链集成（ERC）、多层、多目标回归（MMR）等经典的多目标回归算法进行对比,结果表明所提算法在14个数据集上都取得了最好的性能,并且在18个数据集上的平均性能排序居第一位。可见所提算法构建的目标特定特征能够提高各输出目标的预测准确性,并结合低秩学习得到输出目标间的关联性以从整体上提升多目标回归的预测性能。     

类独立特征提取法在环境声音识别中的应用

语音和非语音类声音的识别在很多系统的研发中都有非常重要的作用,如安全监控、医疗保健、现代化的视听会议系统等。虽然绝大多数声音信号都有其独特的发音机制,然而要从其中进行特征的提取往往缺乏系统有效的方法。基于不同的音频信号都有其固有的特点,使用类所属特征选择方法来提取音频中的特征,从而进行分类,并用所提出的方法对语音和两种非语音类声音（咳嗽和杯碟破碎的声音）进行了实验仿真,实验结果表明,与常规的特征选择方法相比,提出的方法用更少的特征实现了更好的分类。     

Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks

In this paper, we propose a feature detector for the neural network. Our feature detector aims to decompose input patterns into minimum constituents or atomic features. Atomic features are classified into features, common to all the input patterns and features, specific to each pattern. Thus, our feature detector is mainly composed of a common feature detector, distinctive feature detectors. The other two components are an information maximizer and an error minimizer. The distinctive feature detector is realized by the information maximizer, which increases the information, specific to each pattern as much as possible. The error minimizer is a device to minimize the difference between targets and outputs, that is, a usual neural network. We applied our feature detector to two problems: detection of vertical and horizontal bars and the phonological feature detection. In both cases, experimental results confirmed that distinctive features could clearly be extracted and that the common feature detector could extract features, as close as possible to the common features.     

On-the-fly feature importance mining for person re-identification

State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with generic weights, which are assumed to be universally and equally good for all individuals, independent of people's different appearances. In this study, we show that certain features play more important role than others under different viewing conditions. To explore this characteristic, we propose a novel unsupervised approach to bottom-up feature importance mining on-the-fly specific to each re-identification probe target image, so features extracted from different individuals are weighted adaptively driven by their salient and inherent appearance attributes. Extensive experiments on three public datasets give insights on how feature importance can vary depending on both the viewing condition and specific person's appearance, and demonstrate that unsupervised bottom-up feature importance mining specific to each probe image can facilitate more accurate re-identification especially when it is combined with generic universal weights obtained using existing distance metric learning methods.     

基于蚁群参数优化的LightGBM辐射源个体识别

为提升辐射源个体识别正确率和运算效率,提出了一种基于蚁群参数优化的LightGBM辐射源个体识别方法。运用提升小波包变换对辐射源信号数据进行特征提取并构建特征参数体系,对得到的特征数据集进行Z-score标准化处理;以最大分类正确率和最小特征子集规模为目标,建立了LightGBM参数优化和特征选择的数学模型;采用蚁群算法优化LightGBM的6个参数(最小叶子节点数据量、决策树的数量、学习率、L₁正则化项的权重、L₂正则化项的权重和最小叶子节点样本权重和);根据优化后的LightGBM得到每个特征的重要性值并使用序列后向搜索策略进行特征选择;最后通过LightGBM分类器实现对辐射源信号的分类识别。实验结果表明,所提方法在无噪声、信噪比为10 dB和信噪比为5 dB信号的数据集上的识别正确率优于对比特征选择方法GBDT、XGBoost和LightGBM的,同时特征维数的减少也提升了辐射源个体识别的运算效率。     

Two-level hierarchical feature learning for image classification

In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.     

基于本体的特征模型演化的一致性验证

针对特征模型的演化导致特征间的冲突问题, 从特征模型的演化方面进行研究, 提出了一种基于本体的特征模型演化的一致性验证方法。首先采用本体理论方法对特征模型的演化进行分析和建模, 建立本体的特征模型元模型; 其次基于上述元模型, 为网上购物系统实例建立相应的网上购物的领域特征模型, 根据需求裁剪出产品特征模型; 然后在Eclipse集成开发环境下, 通过Jena推理机加载规则和产品特征模型进行一致性验证, 当检测到冲突时, 采用演化策略来消除冲突; 最后通过实例研究说明了该方法的有效性。     

特征模型直接操作技术的研究

目前的语义特征造型系统,由于约束求解的速度比较缓慢,还不能支持直接操作的特性。利用特征依赖图的数据模型来保存和维护特征的信息及其之间的依赖关系,并且提出了约束操作算法和特征操作算法,将特征的操作局限在模型的特定区域内。该方法最大限度地减少了所需要求解的约束数目,满足了直接操作过程中对约束求解速度的要求,从而实现了对特征的直接操作。     

基于特征的产品信息应用模式

企业信息管理系统已经得到极大地普及,随着时间的推移,不少信息管理 系统中逐渐积累了大量的数据。如何便捷有效地利用这些数据成为一个亟待解决的问题。论 文提出一种基于特征的产品数据应用模式,通过特征的抽取、运算,针对性的解决企业信息 管理系统中的数据深层次利用问题。最后,以某装备研发中心的具体案例说明该模式在信息 管理系统中的应用。