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学者姓名：何振峰
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Abstract ：
统的聚类算法能够将数据集划分成不同的簇，但是这些簇通常都是难以解释的.IMM(iterative mistake minimization)是一种常见的可解释聚类算法，通过单个特征来构造阈值树，每个簇都可以用根节点到叶子节点路径上的特征.阈值对进行解释.然而，阈值树在每一轮划分数据时仅考虑错误最少的特征-阈值对，这种贪心的方法容易导致局部最优解.针对这一问题，本文引入了集束搜索，通过在阈值树的每一轮划分过程当中保留预定数量的状态来减缓局部最优，进而提高阈值树提供的聚类划分与初始聚类划分的一致性.最后，通过实验验证了该算法的有效性.
Keyword ：
K-means K-means 可解释聚类 可解释聚类 阈值树 阈值树 集束搜索 集束搜索
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| GB/T 7714 | 李钰群 , 何振峰 . 基于集束搜索的可解释阈值树构造 [C] //第三届 RISC-V 技术及生态研讨会论文集 . 2023 : 247-252 . |
| MLA | 李钰群 等. "基于集束搜索的可解释阈值树构造" 第三届 RISC-V 技术及生态研讨会论文集 . (2023) : 247-252 . |
| APA | 李钰群 , 何振峰 . 基于集束搜索的可解释阈值树构造 第三届 RISC-V 技术及生态研讨会论文集 . (2023) : 247-252 . |
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Abstract ：
基于国内外地铁列车驾驶技术的发展现状,提出并阐述了地铁列车驾驶技术发展的4个阶段为人工驾驶、自动驾驶、无人驾驶、智能无人驾驶.概括了我国无人驾驶地铁列车的建设情况,针对目前基于神经网络这类机器学习方法的列车控制方法可解释性差的弊端,引入了深度模糊系统的概念,提出了基于人机混合智能的地铁智能无人驾驶基本框图,为将处理紧急情况的专家经验、人工智能算法和无人驾驶系统结合起来,实现智能无人驾驶提供了一种具有前景的解决思路.
Keyword ：
人工智能 人工智能 人机混合智能 人机混合智能 地铁 地铁 无人驾驶 无人驾驶 智能无人驾驶 智能无人驾驶
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| GB/T 7714 | 赖文柱 , 陈德旺 , 何振峰 et al. 地铁列车驾驶技术发展综述:从人工驾驶到智能无人驾驶 [J]. | 智能科学与技术学报 , 2022 , 4 (3) : 335-343 . |
| MLA | 赖文柱 et al. "地铁列车驾驶技术发展综述:从人工驾驶到智能无人驾驶" . | 智能科学与技术学报 4 . 3 (2022) : 335-343 . |
| APA | 赖文柱 , 陈德旺 , 何振峰 , 邓新国 , GIUSEPPE CARLO Marano . 地铁列车驾驶技术发展综述:从人工驾驶到智能无人驾驶 . | 智能科学与技术学报 , 2022 , 4 (3) , 335-343 . |
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Abstract ：
FSSD (fast and efficient subgroup set discovery)是一种子群发现算法, 旨在短时间内提供多样性模式集, 然而此算法为了减少运行时间, 选择域数量少的特征子集, 当特征子集与目标类不相关或者弱相关时, 模式集质量下降. 针对这个问题, 提出一种基于集成特征选择的FSSD算法, 它在预处理阶段使用基于ReliefF (Relief-F)和方差分析的集成特征选择来获得多样性和相关性强的特征子集, 再使用FSSD算法返回高质量模式集. 在UCI数据集、全国健康和营养调查报告(NHANES)数据集上的实验结果表明, 改进后的FSSD算法提高了模式集质量, 归纳出更有趣的知识. 在NHANES数据集上, 进一步分析模式集的特征有效性和阳性预测值.
Keyword ：
ReliefF ReliefF 子群发现 子群发现 方差分析 方差分析 集成特征选择 集成特征选择
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| GB/T 7714 | 张崟 , 何振峰 . 基于集成特征选择的FSSD算法 [J]. | 计算机系统应用 , 2022 , 31 (3) : 275-281 . |
| MLA | 张崟 et al. "基于集成特征选择的FSSD算法" . | 计算机系统应用 31 . 3 (2022) : 275-281 . |
| APA | 张崟 , 何振峰 . 基于集成特征选择的FSSD算法 . | 计算机系统应用 , 2022 , 31 (3) , 275-281 . |
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Subgroup discovery (SD) identifies disproportionally distributed subsets from a dataset according to a target concept. Numerical features are often discretized before SD to avoid generating too many interval based patterns and aggravating the 'pattern flooding' problem. However, early discretization greatly reduces the quality of subgroups. The addition of a few features, especially numerical features, often sharply prolongs the running time of SD, so removing irrelevant features may be a better choice. FSSD, a recently proposed non-discretization SD approach for numerical features, uses an empirical method to select a subset of features. Yet, the method ignores the labelling information, so it can not remove irrelevant features effectively. This paper analyses Relief based feature selection for SD, and suggests using interval based local subgroups to evaluate the discrimination ability of a feature. It presents ReliefSD, a novel feature selection method for SD by updating ReliefF. As interesting subgroups have many positive instances, ReliefSD only selects positive instances. Moreover, for each feature ReliefSD constructs a single feature based local subgroup whose boundary is defined by the randomly selected instance and its neighbouring positive instances. By evaluating the purity of the subgroups, ReliefSD iteratively estimates the importance of features. Experimental results on 10 UCI datasets suggest ReliefSD is the best in selecting feature subsets for FSSD when compared with the empirical method and ReliefF. © 2022 Technical Committee on Control Theory, Chinese Association of Automation.
Keyword ：
Feature Selection Feature Selection Iterative methods Iterative methods Numerical methods Numerical methods
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| GB/T 7714 | He, Zhenfeng , Zhang, Yin . ReliefSD: Selecting Numerical Features for Fast Subgroup Discovery [C] . 2022 : 3214-3219 . |
| MLA | He, Zhenfeng et al. "ReliefSD: Selecting Numerical Features for Fast Subgroup Discovery" . (2022) : 3214-3219 . |
| APA | He, Zhenfeng , Zhang, Yin . ReliefSD: Selecting Numerical Features for Fast Subgroup Discovery . (2022) : 3214-3219 . |
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Abstract ：
基于国内外地铁列车驾驶技术的发展现状,阐述了地铁列车智能驾驶发展及研究的必要性.针对当前无人驾驶采用的机器学习算法可解释性差的缺陷,引入模糊系统,提出了基于人机混合智能的地铁列车无人驾驶系统,以两种方式实现人机混合智能.探索了结合认知系统的地铁列车无人驾驶系统,为实现真正意义上的强人工智能地铁列车无人驾驶系统提供了一种面向未来的解决方案.
Keyword ：
人机混合智能 人机混合智能 地铁 地铁 无人驾驶 无人驾驶
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| GB/T 7714 | 黄本遵 , 陈德旺 , 何振峰 et al. 基于人机混合智能的地铁列车无人驾驶系统研究 [J]. | 智能科学与技术学报 , 2022 , 4 (4) : 584-591 . |
| MLA | 黄本遵 et al. "基于人机混合智能的地铁列车无人驾驶系统研究" . | 智能科学与技术学报 4 . 4 (2022) : 584-591 . |
| APA | 黄本遵 , 陈德旺 , 何振峰 , 邓新国 , GIUSEPPE CARLO Marano . 基于人机混合智能的地铁列车无人驾驶系统研究 . | 智能科学与技术学报 , 2022 , 4 (4) , 584-591 . |
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Weighted K-Means (WKM) algorithms are increasingly important with the increase of data dimension. WKM faces an initialization problem that is more complicated than K-Means' because in addition to picking initial cluster centers, it should also provide feature weights. Moreover, the one-dimensional solution to WKM's widely used objective function is unacceptable in most cases. Yet, the initialization of WKM, especially the initialization of feature weight, has been largely ignored. This paper studies the problem by analyzing Feature weight self-adjustment K-Means(FWSA K-Means), a popular WKM proposed to avoid the one-dimensional solution. Experimental results suggest that the algorithm is actually easy to cluster mainly based on a single feature information when it is not well initialized. Moreover, the paper argues that initial feature weights and cluster centers are equally important in determining the final partition. Therefore it suggests using feature level constraints to improve the initialization and proposes a semi-supervised algorithm Constrained FWSA K-Means (CFWSA K-Means). The algorithm uses constraints in evaluating feature weights and clusters to guide their evolution at the stage of initialization. Experimental results suggest that it is effective and robust in utilizing constraints. In addition, if its initialization process is started by the cluster centers provided by BRIk, an initialization approach for K-Means, the performance can be further improved.
Keyword ：
feature level constraint feature level constraint initialization initialization semi-supervised clustering semi-supervised clustering Weighted K-Means Weighted K-Means
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| GB/T 7714 | He, Zhenfeng . Initializing FWSA K-Means With Feature Level Constraints [J]. | IEEE ACCESS , 2022 , 10 : 132976-132987 . |
| MLA | He, Zhenfeng . "Initializing FWSA K-Means With Feature Level Constraints" . | IEEE ACCESS 10 (2022) : 132976-132987 . |
| APA | He, Zhenfeng . Initializing FWSA K-Means With Feature Level Constraints . | IEEE ACCESS , 2022 , 10 , 132976-132987 . |
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在软件定义网络(SDN)环境下,通过深度包检测(DPI)技术对网络流量进行分类识别.在应用层部署的DPI功能节点越多,网络流量分析的总处理时延就越小.但由于DPI节点的部署成本昂贵,因此需要优化部署方案.针对SDN中DPI节点部署问题,根据QoS处理时延,对DPI节点进行负载均衡,将该问题抽象为多商品流的线性规划问题,并设计基于成本的两阶段贪心算法加以解决.通过贪心策略为入口交换机选择低成本的DPI节点,并针对流处理过程进行负载均衡,进而通过搜素算法寻找最短路径用以降低时延.实验结果表明,该方法优化了DPI节点部署成本,提升了流量分析与检测的请求成功率.
Keyword ：
QoS时延 QoS时延 深度包检测 深度包检测 线性规划 线性规划 节点部署 节点部署 负载均衡 负载均衡 软件定义网络 软件定义网络
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| GB/T 7714 | 朱丹红 , 李洪 , 张栋 et al. 软件定义网络中DPI功能节点的部署研究 [J]. | 现代电子技术 , 2021 , 44 (4) : 119-123 . |
| MLA | 朱丹红 et al. "软件定义网络中DPI功能节点的部署研究" . | 现代电子技术 44 . 4 (2021) : 119-123 . |
| APA | 朱丹红 , 李洪 , 张栋 , 何振峰 . 软件定义网络中DPI功能节点的部署研究 . | 现代电子技术 , 2021 , 44 (4) , 119-123 . |
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Evolutionary K-Means (EKM), which combines K-Means and genetic algorithm, solves K-Means' initiation problem by selecting parameters automatically through the evolution of partitions. Currently, EKM algorithms usually choose silhouette index as cluster validity index, and they are effective in clustering well-separated clusters. However, their performance of clustering noisy data is often disappointing. On the other hand, clustering stability-based approaches are more robust to noise; yet, they should start intelligently to find some challenging clusters. It is necessary to join EKM with clustering stability-based analysis. In this paper, we present a novel EKM algorithm that uses clustering stability to evaluate partitions. We firstly introduce two weighted aggregated consensus matrices, positive aggregated consensus matrix (PA) and negative aggregated consensus matrix (NA), to store clustering tendency for each pair of instances. Specifically, PA stores the tendency of sharing the same label and NA stores that of having different labels. Based upon the matrices, clusters and partitions can be evaluated from the view of clustering stability. Then, we propose a clustering stability-based EKM algorithm CSEKM that evolves partitions and the aggregated matrices simultaneously. To evaluate the algorithm's performance, we compare it with an EKM algorithm, two consensus clustering algorithms, a clustering stability-based algorithm and a multi-index-based clustering approach. Experimental results on a series of artificial datasets, two simulated datasets and eight UCI datasets suggest CSEKM is more robust to noise.
Keyword ：
Clustering Clustering Clustering stability Clustering stability Consensus clustering Consensus clustering Genetic algorithm Genetic algorithm K-Means algorithm K-Means algorithm
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| GB/T 7714 | He, Zhenfeng , Yu, Chunyan . Clustering stability-based Evolutionary K-Means [J]. | SOFT COMPUTING , 2019 , 23 (1) : 305-321 . |
| MLA | He, Zhenfeng et al. "Clustering stability-based Evolutionary K-Means" . | SOFT COMPUTING 23 . 1 (2019) : 305-321 . |
| APA | He, Zhenfeng , Yu, Chunyan . Clustering stability-based Evolutionary K-Means . | SOFT COMPUTING , 2019 , 23 (1) , 305-321 . |
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Disproportionality based methods are widely used in detecting occupational hazard signals. Yet, deciding on a proper background is hard because there are subgroups vulnerable to given exposures, and in many cases such subgroups can not be defined by job. This paper presents an interactive way to do disproportionality analysis on occupational health examination data. It firstly uses multi-dimensional scaling to project data from a subspace to a two-dimensional space, which is further divided into rectangle cells. Then each cell is served as a background to evaluate disproportionality for potential hazard record(PHR), which is jointly defined by a cell and a company. The PHRs with high disproportionalities are checked interactively to select interesting ones. The method has been used to analyse an occupational hearing loss data, and has found an interesting potential signal for further analysis.
Keyword ：
data visualization data visualization disproportionality analysis disproportionality analysis multidimensional scaling multidimensional scaling occupational hazard signal detection occupational hazard signal detection
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| GB/T 7714 | He, Zhenfeng , Lan, Shuxiu , Shen, Bo . Interactive detection of potential occupational hazard signals [C] . 2019 : 109-112 . |
| MLA | He, Zhenfeng et al. "Interactive detection of potential occupational hazard signals" . (2019) : 109-112 . |
| APA | He, Zhenfeng , Lan, Shuxiu , Shen, Bo . Interactive detection of potential occupational hazard signals . (2019) : 109-112 . |
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Relief算法是一个过滤式特征选择算法,通过一种贪心的方式最大化最近邻居分类器中的实例边距,结合局部权重方法有作者提出了为每个类别分别训练一个特征权重的类依赖Relief算法(Class Dependent RELIEF algorithm:CDRELIEF).该方法更能反映特征相关性,但是其训练出的特征权重仅仅对于衡量特征对于某一个类的相关性很有效,在实际分类中分类精度不够高.为了将CDRELIEF算法应用于分类过程,本文改变权重更新过程,并给训练集中的每个实例赋予一个实例权重值,通过将实例权重值结合到权重更新公式中从而排除远离分类边界的数据点和离群点对权重更新的影响,进而提高分类准确率.本文提出的实例加权类依赖RELIEF(IWCDRELIEF)在多个UCI二类数据集上,与CDRELIEF进行测试比较.实验结果表明本文提出的算法相比CDRELIEF算法有明显的提高.
Keyword ：
Relief算法 Relief算法 分类 分类 实例加权 实例加权 局部权重 局部权重 特征加权 特征加权
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| GB/T 7714 | 邱海峰 , 何振峰 . 实例加权类依赖Relief [J]. | 计算机系统应用 , 2019 , 28 (7) : 121-126 . |
| MLA | 邱海峰 et al. "实例加权类依赖Relief" . | 计算机系统应用 28 . 7 (2019) : 121-126 . |
| APA | 邱海峰 , 何振峰 . 实例加权类依赖Relief . | 计算机系统应用 , 2019 , 28 (7) , 121-126 . |
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