基于BPSO-NB算法的Android恶意应用检测方法

为了提高Android恶意应用检测效率,将二值粒子群算法(BPSO,Binary Particle Swarm Optimization)用于原始特征全集的优化选择,并结合朴素贝叶斯(NB,Nave Bayesian)分类算法,提出一种基于BPSO-NB的Android恶意应用检测方法。该方法首先对未知应用进行静态分析,提取AndroidManifest.xml文件中的权限信息作为特征。然后,采用BPSO算法优化选择分类特征,并使用NB算法的分类精度作为评价函数。最后采用NB分类算法构建Android恶意应用分类器。实验结果表明,通过二值粒子群优化选择分类特征可以有效提高分类精度,缩短检测时间。      

Particle swarm optimization-based feature selection in sentiment classification

Sentiment classification is one of the important tasks in text mining, which is to classify documents according to their opinion or sentiment. Documents in sentiment classification can be represented in the form of feature vectors, which are employed by machine learning algorithms to perform classification. For the feature vectors, the feature selection process is necessary. In this paper, we will propose a feature selection method called fitness proportionate selection binary particle swarm optimization (F-BPSO). Binary particle swarm optimization (BPSO) is the binary version of particle swam optimization and can be applied to feature selection domain. F-BPSO is a modification of BPSO and can overcome the problems of traditional BPSO including unreasonable update formula of velocity and lack of evaluation on every single feature. Then, some detailed changes are made on the original F-BPSO including using fitness sum instead of average fitness in the fitness proportionate selection step. The modified method is, thus, called fitness sum proportionate selection binary particle swarm optimization (FS-BPSO). Moreover, further modifications are made on the FS-BPSO method to make it more suitable for sentiment classification-oriented feature selection domain. The modified method is named as SCO-FS-BPSO where SCO stands for “sentiment classification-oriented”. Experimental results show that in benchmark datasets original F-BPSO is superior to traditional BPSO in feature selection performance and FS-BPSO outperforms original F-BPSO. Besides, in sentiment classification domain, SCO-FS-BPSO which is modified specially for sentiment classification is superior to traditional feature selection methods on subjective consumer review datasets.     

Improved binary particle swarm optimization using catfish effect for feature selection

The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space (“catfish particles”), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to 10 classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.     

多层次降维的头颈癌图像特征选择方法

针对原始病理图像经软件提取形态学特征后存在高维度,以及医学领域上样本的少量性问题,提出ReliefF-HEPSO头颈癌病理图像特征选择算法。该算法构建了多层次降维框架,首先根据特征和类别的相关性,利用ReliefF算法确定不同的特征权重,实现初步降维。其次利用进化神经策略(ENS)丰富二进制粒子群算法(BPSO)的种群的多样性,提出混合二进制进化粒子群算法(HEPSO)对候选特征子集完成最佳特征子集的自动寻找。与7种特征选择算法的实验对比结果证明,该算法能更有效筛选出高相关性的病理图像形态学特征,实现快速降维,以较少特征获得较高分类性能。     

改进粒子群算法应用于Android恶意应用检测

为进行Android恶意应用检测,提取了Android应用程序的API调用信息、申请权限信息、Source-Sink信息为特征,这些信息数量庞大,特征维数高达三四万维。为消除冗余特征和减少分类器构建时间,提出了使用[L1]与离散二进制粒子群算法（BPSO）进行混合式特征选择;同时针对BPSO易早熟收敛的缺点,提出了一种改进的二进制粒子群算法SVBPSO。通过研究不同映射函数对二进制粒子群算法的影响发现,使用S型映射函数的BPSO全局搜索能力强,使用V型映射函数的BPSO局部搜索能力强,故该算法使用S型映射函数进行全局搜索,每隔一定迭代次数使用V型映射函数进行局部探索。实验结果证明,SVBPSO具有良好的收敛效果,使用SVBPSO进行特征选择后能提高Android恶意应用检测正确率。     

Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we propose an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies. Using several well-known medical diagnosis problems of breast cancer, diabetes, and erythemato-squamous, the proposed SVM model, termed CMWOAFS-SVM, was compared with multiple competitive SVM models based on other optimization algorithms including the original algorithm, particle swarm optimization, bacterial foraging optimization, and genetic algorithms. The experimental results demonstrate that CMWOAFS-SVM significantly outperformed all the other competitors in terms of classification performance and feature subset size.     

Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients

This paper proposes a modified binary particle swarm optimization (MBPSO) method for feature selection with the simultaneous optimization of SVM kernel parameter setting, applied to mortality prediction in septic patients. An enhanced version of binary particle swarm optimization, designed to cope with premature convergence of the BPSO algorithm is proposed. MBPSO control the swarm variability using the velocity and the similarity between best swarm solutions. This paper uses support vector machines in a wrapper approach, where the kernel parameters are optimized at the same time. The approach is applied to predict the outcome (survived or deceased) of patients with septic shock. Further, MBPSO is tested in several benchmark datasets and is compared with other PSO based algorithms and genetic algorithms (GA). The experimental results showed that the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy, specially when compared to other PSO based algorithms. When compared to GA, MBPSO is similar in terms of accuracy, but the subset solutions have less selected features.     

基于多目标骨架粒子群优化的特征选择算法

针对在分类问题中,数据之间存在大量的冗余特征,不仅影响分类的准确性,而且会降低分类算法执行速度的问题,提出了一种基于多目标骨架粒子群优化（BPSO）的特征选择算法,以获取在特征子集个数与分类精确度之间折中的最优策略。为了提高多目标骨架粒子群优化算法的效率,首先使用了一个外部存档,用来引导粒子的更新方向;然后通过变异算子,改善粒子的搜索空间;最后,将多目标骨架粒子群算法应用到特征选择问题中,并利用K近邻（KNN）分类器的分类性能和特征子集的个数作为特征子集的评价标准,对UCI数据集以及基因表达数据集的12个数据集进行实验。实验结果表明,所提算法选择的特征子集具有较好的分类性能,最小分类错误率最大可以降低7.4%,并且分类算法的执行时间最多能缩短12 s,能够有效提高算法的分类性能与执行速度。     

二进制粒子群算法在人脸识别中的应用

把二进制粒子群优化算法(BPSO)应用到人脸识别中.对人脸图像进行二维离散余弦变换(DCT),获得人脸图像的特征向量,应用BPSO算法对得到的特征向量进行特征选择,得到最具代表性的人脸特征.与遗传算法(GA)相比,在选择的特征较少的情况下,BPSO算法比遗传算法有更好的识别率.实验结果表明,BPSO算法应用到人脸识别中有较高的识别率,是一种非常有效的特征提取方法.     

一种基于支持向量的二进制粒子群网络故障特征选择算法

网络故障诊断中大量无关或冗余的特征会降低诊断的精度,需要对初始特征进行选择。Wrapper模式特征选择方法分类算法计算量大,为了降低计算量,本文提出了基于支持向量的二进制粒子群（SVB-BPSO）的故障特征选择方法。该算法以SVM为分类器,首先通过对所有样本的SVM训练选出SV集,在封装的分类训练中仅使用SV集,然后采用异类支持向量之间的平均距离作为SVM的参数进行训练,最后根据分类结果,利用BPSO在特征空间中进行全局搜索选出最优特征集。在DARPA数据集上的实验表明本文提出的方法能够降低封装模式特征选择的计算量且获得了较高的分类精度以及较明显的降维效果。     

The continuous artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABC_bin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABC_bin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABC_bin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABC_bin is an alternative and simple binary optimization tool in terms of solution quality and robustness.     

基于改进斑点鬣狗优化算法的同步优化特征选择

针对传统支持向量机（SVM）在封装式特征选择中分类精度低、特征子集选择冗余以及计算效率差的不足,利用元启发式优化算法同步优化SVM与特征选择。为改善SVM分类效果以及选择特征子集的能力,首先,利用自适应差分进化（DE）算法、混沌初始化与锦标赛选择策略对斑点鬣狗优化（SHO）算法改进,以增强其局部搜索能力并提高其寻优效率与求解精度;其次,将改进后的算法用于特征选择与SVM参数调整的同步优化中;最后,在UCI数据集进行特征选择仿真实验,采取分类准确率、选择特征数、适应度值及运行时间来综合评估所提算法的优化性能。实验结果证明,改进算法的同步优化机制能够在高分类准确率下降低特征选择的数目,该算法比传统算法更适合解决封装式特征选择问题,具有良好的应用价值。     

PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis

Linear discriminant analysis (LDA) is a commonly used classification method. It can provide important weight information for constructing a classification model. However, real-world data sets generally have many features, not all of which benefit the classification results. If a feature selection algorithm is not employed, unsatisfactory classification will result, due to the high correlation between features and noise. This study points out that the feature selection has influence on the LDA by showing an example. The methods traditionally used for LDA to determine the beneficial feature subset are not easy or cannot guarantee the best results when problems have larger number of features.The particle swarm optimization (PSO) is a powerful meta-heuristic technique in the artificial intelligence field; therefore, this study proposed a PSO-based approach, called PSOLDA, to specify the beneficial features and to enhance the classification accuracy rate of LDA. To measure the performance of PSOLDA, many public datasets are employed to measure the classification accuracy rate. Comparing the optimal result obtained by the exhaustive enumeration, the PSOLDA approach can obtain the same optimal result. Due to much time required for exhaustive enumeration when problems have larger number of features, exhaustive enumeration cannot be applied. Therefore, many heuristic approaches, such as forward feature selection, backward feature selection, and PCA-based feature selection are used. This study showed that the classification accuracy rates of the PSOLDA were higher than those of these approaches in many public data sets.     

融合降噪自编码器与BPSO的特征组合方法及其中医应用

离散二进制粒子群算法（BPSO）在各种离散优化问题中有着诸多优势,但其很容易由于非线性的问题陷入局部最优解,无法得到最佳特征子集。而降噪自编码器可通过多层非线性网络进行映射与重构,对中医药数据有良好的处理效果。因此提出了一种融合降噪自编码器与BPSO的特征组合方法,该方法主要是利用降噪自编码器对特征进行非线性映射形成超完备基,然后在超完备基中通过BPSO进行搜索,从而得到最佳特征子集。分别采用临床糖尿病数据集和UCI数据集进行分析处理,实验结果表明,融合降噪自编码器与BPSO的特征组合方法对中医药临床实验数据有较好的适应性。     

Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits

State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae. In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization. It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm. Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova.     

二进制粒子群优化算法在化工优化问题中的应用

优化问题是化工过程的一个主要问题,而由化工问题建模所得到的优化问题大多较为复杂,此时要求的优化算法具有良好的优化性能。粒子群优化算法是新近发展起来的一种优化算法,但其对多极值函数的优化时,易陷局部极值。本文在分析粒子群优化算法的机理、考虑二进制比十进制更易于学习等的基础上,提出采用二进制表示粒子群优化算法,使每个粒子更易于从个体极值与全局极值中学习,从而使算法具有更强的搜索能力与更快的收敛速度,性能测试说明了所提出的算法是有效的．最后将算法用于求解换热网络的优化问题,取得良好效果。     

改进粒子群算法优化支持向量机在故障诊断中的应用研究

支持向量机(SVM)作为当前新型的机器学习方式,凭借解决小样本问题、高维问题和局部极值问题等方面的优越性,在当前故障诊断方面有突出的表现;文章根据对支持向量机的研究,发现其在分类模型参数选择上存在困难,为此,提出利用改进粒子群算法优化的办法,解决粒子群前期收敛速度过快导致后期容易优化不均的现象;通过粒子群算法优化与支持向量机分类模型结合,以轴承故障检测和诊断为例,分析次方法的优越性和提高支持向量机在故障诊断过程中的精准度;通过实际检测得出,这种算法优化的方法改进的支持向量机对于聚类性较差的故障分类具有很好的诊断功能。     

多目标优化在特征选择子集评价中的应用

特征选择是处理高维大数据常用的降维手段,但其中牵涉到的多个彼此冲突的特征子集评价目标难以平衡。为综合考虑特征选择中多种子集评价方式间的折中,优化子集性能,提出一种基于子集评价多目标优化的特征选择框架,并重点对多目标粒子群优化（MOPSO）在特征子集评价中的应用进行了研究。该框架分别根据子集的稀疏度、分类能力和信息损失度设计多目标优化函数,继而基于多目标优化算法进行特征权值向量寻优,并通过权值向量Pareto解集膝点选取确定最优向量,最终实现基于权值向量排序的特征选择。设计实验对比了基于多目标粒子群优化算法的特征选择（FS_MOPSO）与四种经典方法的性能,多个数据集上的结果表明,FS_MOPSO在低维空间表现出更高的分类精度,并保证了更少的信息损失。     

A new particle swarm feature selection method for classification

Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to solve such problems efficiently. This study proposes a regression-based particle swarm optimization for feature selection problem. The proposed algorithm can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms.     

Particle Swarm Optimization with Chaos-based Initialization for Numerical Optimization

Particle swarm optimization (PSO) is a population based swarm intelligence algorithm that has been deeply studied and widely applied to a variety of problems. However, it is easily trapped into the local optima and premature convergence appears when solving complex multimodal problems. To address these issues, we present a new particle swarm optimization by introducing chaotic maps (Tent and Logistic) and Gaussian mutation mechanism as well as a local re-initialization strategy into the standard PSO algorithm. On one hand, the chaotic map is utilized to generate uniformly distributed particles to improve the quality of the initial population. On the other hand, Gaussian mutation as well as the local re-initialization strategy based on the maximal focus distance is exploited to help the algorithm escape from the local optima and make the particles proceed with searching in other regions of the solution space. In addition, an auxiliary velocity-position update strategy is exclusively used for the global best particle, which can effectively guarantee the convergence of the proposed particle swarm optimization. Extensive experiments on eight well-known benchmark functions with different dimensions demonstrate that the proposed PSO is superior or highly competitive to several state-of-the-art PSO variants in dealing with complex multimodal problems.