Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

Genetic-Frog-Leaping Algorithm for Text Document Clustering

In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web.As a result,the use of techniques for extracting useful information from large collections of data,and particularly documents,has become more necessary and challenging.Text clustering is such a technique;it consists in dividing a set of text documents into clusters(groups),so that documents within the same cluster are closely related,whereas documents in different clusters are as different as possible.Clustering depends on measuring the content(i.e.,words)of a document in terms of relevance.Nevertheless,as documents usually contain a large number of words,some of them may be irrelevant to the topic under consideration or redundant.This can confuse and complicate the clustering process and make it less accurate.Accordingly,feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features.In this study,we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features.The proposed approach is based on two metaheuristic algorithms:a genetic algorithm(GA)and a shuffled frog-leaping algorithm(SFLA).The GA performs feature selection,and the SFLA performs clustering.To evaluate its effectiveness,the proposed approach was tested on a well-known text document dataset:the“20Newsgroup”dataset from the University of California Irvine Machine Learning Repository.Overall,after multiple experiments were compared and analyzed,it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering,compared with classical K-means clustering.Nevertheless,this improvement requires longer computational time.

Unsupervised spectral feature selection algorithms for high dimensional data

It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of samples.Feature selection especially the unsupervised ones are the right way to deal with this challenge and realize the task.Therefore,two unsupervised spectral feature selection algorithms are proposed in this paper.They group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as possible.Then two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be built.The effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer methods.The extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking technique.The statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs best.The detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and explainable.It is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.

A hierarchical clustering approach for colorectal cancer molecular subtypes identification from gene expression data

Background Colorectal cancer(CRC)is the second leading cause of cancer fatalities and the third most common human disease.Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly.Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.Methods This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods.The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal.Agglomerative hierarchy clustering was used to identify molecular subtypes,with a P-value-based approach for feature selection.The performance of the model was evaluated using various classifiers including multilayer perceptron(MLP).Results The proposed methodology outperformed conventional methods,with the MLP classifier achieving the highest accuracy of 89%after feature selection.The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.Conclusion This method could aid in developing tailored therapeutic strategies for CRC patients,although there is a need for further validation and evaluation of its clinical significance.

An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers

Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.

基于改进XGBoost的金融客户投资行为特征选择方法

金融客户投资购买行为是投资者动态购买理财产品交易决策的综合结果,受到客户自身属性、产品因素、行情信息和历史交易等多个不同因素的影响,原始因子属性的特征维度庞大、拟合风险偏高。现有研究主要通过不同的算法提高特征选择的准确率,忽略了不同群体的差异化特征及动态因素的影响。因此,提出一种改进XGBoost(eXtreme Gradient Boosting)的特征选择算法,并在金融客户投资行为上应用研究。针对客户群体投资行为的差异性,多维度综合量化分析投资行为,以解决单一投资行为指标不合理问题;对不同客户群体通过主成分分析(PCA)降维和优化的K-均值(K-means)聚类算法进行多属性融合聚类,然后分别对聚类后的不同群体使用改进XGBoost进行多分类预测,并通过修剪特征因子提升预测准确率。实验结果表明,使用改进XGBoost后,金融客户投资行为的特征因子维度更贴近实际,准确率更高。

基于谱聚类算法的高速网络数据流快速分类方法研究

当前高速网络数据流分类处理时,忽略了冗余数据对分类结果的影响,使得分类结果 F1值较低。因此,提出了基于谱聚类算法的高速网络数据流快速分类方法。采用主成分分析法对高速网络数据流进行降维处理。对所有数据流相关性特征进行选择,去除冗余特征,保留有效的特征信息。应用支持向量机算法构建网络数据流快速分类模型,结合谱聚类算法对多数类样本进行聚类,组成新的数据集并将其输入到分类模型中得出相关的分类结果。实验结果表明,所提方法的平均F1值为0.95,F1值越大分类结果越准确,说明该方法能够满足高速网络数据流快速准确分类,具有优越的数据分类性能,应用价值更高。

PCCS部分聚类分类:一种快速的Web文档聚类方法

PCCS是为了帮助 Web用户从搜索引擎所返回的大量文档片断中筛选出自己所需要的文档 ,而使用的一种对 Web文档进行快速聚类的部分聚类方法 :首先对一部分文档进行聚类 ,然后根据聚类结果形成分类模型对其余的文档进行分类 .采用交互式的一次改进一个聚类摘选的聚类方法快速地创建一个聚类摘选集 ,将其余的文档使用 Nal¨ve- Bayes分类器进行划分 .为了提高聚类与分类的效率 ,提出了一种混合特征选取方法以减少文档表示的维数 :重新计算文档中各特征的熵 ,从中选取具有最大熵值的前若干个特征 ;或者基于持久分类模型中的特征集来进行特征选取 .实验证明 ,部分聚类方法能够快速、准确地根据文档主题内容组织 Web文档 ,使用户在更高的主题层次上来查看搜索引擎返回的结果 。

基于VSM的文本相似度计算的研究

文本相似度的计算作为其他文本信息处理的基础和关键,其计算准确率和效率直接影响其他文本信息处理的结果。提出改进的DF算法和TD-IDF算法,一方面利用了DF算法具有线性的时间复杂度,比较适合大规模文本处理的特点,并通过适当增加关键词的方法,弥补了其对个别有用信息错误过滤的不足;另一方面,利用特征项在特征选择阶段的权重对TD-IDF方法进行加权处理,在不增加开销的情况下扩大了文档集的规模,还提高了相似度计算的精确度。

中文文本分类中基于词性的特征提取方法研究

在介绍常用的文本分类中特征词提取方法的基础上,提出了一种全新的,适用于中文文本分类的特征提取方法———基于词性的特征提取方法,实验结果显示,这种基于词性的特征提取方法在提高特征提取效率和降低特征向量维数方面都有显著改善。

基于特征相关性的特征选择

提出了一种基于特征相关性的特征选择方法。该方法以特征之间相互依赖程度(相关度)为聚类依据先对特征进行聚类,再从各特征簇中挑选出具有代表性的特征,然后在被选择出来的特征中删除与目标特征无关或是弱相关的特征,最后留下的特征作为最终的特征子集。理论分析表明该方法的运算效率高,时间复杂度低,适合于大规模数据集中的特征选择。在UCI数据集上与文献中的经典方法进行实验比较和分析,结果显示提出的特征选择方法在特征约减和分类等方面具有更好的性能。

基于支持向量机的Web文本分类方法

Web文本分类技术是数据挖掘中一个研究热点领域,而支持向量机又是一种高效的分类识别方法,在解决高维模式识别问题中表现出许多特有的优势。文章通过分析Web文本的特点,研究了向量空间模型(VSM)的分类方法和核函数的选取,在此基础上结合决策树方法提出了一种基于决策树支持向量机的Web文本分类模型,并给出具体的算法。通过实验测试表明,该方法训练数据规模大大减少,训练效率较高,同时具有较好的精确率(90.11%)和召回率(89.38%)。

基于分类规则的C4.5决策树改进算法

为解决大样本数据条件下C4.5决策树算法需要训练集常驻内存、分类精度达不到需求以及如何选取最优分类规则等问题,提出了一种基于分类规则选取的C4.5决策树改进算法。通过数次有放回的随机抽取训练集形成多个分类规则,在多次分类规则内寻找特征的最优取值以建立最优分类规则,以划分相似度为标准进行C4.5决策树最优特征选取,在此基础上利用选定的最优分类规则和最优特征对C4.5决策树算法进行改进。实验结果表明,改进后的算法可有效解决C4.5决策树与初始训练集相关性较大的问题,对大样本数据集的分类识别在识别率上有显著提高,训练时间明显减少。

基于KNN的Web文本分类方法的研究

为了更有效地组织Internet上丰富的信息资源,通过分析Web文本的特点,提出了基于KNN的Web文本分类方法,并结合具体实验在对数据进行预处理的基础上实现了KNN分类算法。实验表明,该方法训练数据规模大大减少,训练效率较高,同时具有较好的精确率和召回率。

高维聚类中的一种特征筛选方法

聚类分析是数据挖掘领域中一个基础而活跃的研究课题。由于大多数的聚类方法在处理高维数据时会出现高维失效问题,维简约成为高维聚类中一个非常重要的处理步骤。通过分析对象间相似性度量与原始数据分布间的关系,提出一种基于熵的特征筛选方法。该方法通过构造一个基于对象间相似度的熵度量,对原始特征集中的每个特征进行重要性评估,从而获得重要特征子集。实验结果显示,该方法可以有效剔除高维数据集中的不重要或噪声特征,改善聚类算法的性能和聚类结果的可理解性。

基于文档频率的特征选择方法

传统的文档频率(DF)方法在进行特征选择时仅考虑特征词在类别中出现的DF,没有考虑特征词在每篇文档中出现的词频率(TF)问题。针对该问题,基于特征词在每篇文档中出现的TF,结合特征词在类别中出现的DF提出特征选择的新算法,并使用支持向量机方法训练分类器。实验结果表明,在进行特征选择时,考虑高词频特征词对类别的贡献,可提高传统DF方法的分类性能。

一种基于中心文档的KNN中文文本分类算法

在浩瀚的数据资源中,为了实现对特定主题的搜索或提取,文本自动分类技术已经成为目前研究的热点。KNN是一种重要的文本自动分类方法,KNN能够处理大规模数据,且具有较高的稳定性,但面临分类速度较慢的问题。以KNN方法为基础,引入特征项间的语义关系,并根据语义关系进行聚类生成中心文档,减少了KNN要搜索的文档数,提高了分类速度。仿真实验表明,该算法在不损失分类精度的情况下,显著提高了分类的速度。

基于特征相似度的贝叶斯网络入侵检测方法

传统贝叶斯入侵检测方法未考虑属性和属性权值对检测结果的影响。为此,提出基于特征相似度的贝叶斯网络入侵检测方法。利用相似度对网络连接数据的属性特征进行选择,抽取其关键特征,并降低属性的冗余度,以优化朴素贝叶斯的分类性能。实验结果表明,该方法能降低分类数据的维数,提高分类的准确率。

一种基于特征聚类的特征选择方法

特征选择是数据挖掘和机器学习领域中一种常用的数据预处理技术。在无监督学习环境下,定义了一种特征平均相关度的度量方法,并在此基础上提出了一种基于特征聚类的特征选择方法 FSFC。该方法利用聚类算法在不同子空间中搜索簇群,使具有较强依赖关系(存在冗余性)的特征被划分到同一个簇群中,然后从每一个簇群中挑选具有代表性的子集共同构成特征子集,最终达到去除不相关特征和冗余特征的目的。在UCI数据集上的实验结果表明,FSFC方法与几种经典的有监督特征选择方法具有相当的特征约减效果和分类性能。

基于内容的图像检索技术研究

随着数字图像在多媒体领域的广泛应用,对基于内容的图像检索技术的需求也不断增加。基于内容的图像检索技术总体上可以分为两部分:图像特征提取、图像特征的索引与匹配。图像特征提取主要解决如何在数学上有效地描述一幅图像。文中分别介绍了颜色、形状和纹理特征提取算法近年来的研究成果。图像特征索引与匹配,主要解决如何根据特征描述判断图像间的相似程度,并准确、快速列出图像库中与检索图像相似的图像,分别介绍了相似度测量方法、聚类与分类技术、相关反馈技术三类技术的主要研究成果。最后对基于内容的图像检索技术的研究难点进行了讨论,对未来可能的研究方向进行了展望。