一种基于多进化神经网络的分类方法

分类问题是目前数据挖掘和机器学习领域的重要内容.提出了一种基于多进化神经网络的分类方法CABEN(classification approach based on evolutionary neural networks).利用改进的进化策略和Levenberg-Marquardt方法对多个三层前馈神经网络同时进行训练.训练好各个分类模型以后,将待识别数据分别输入,最后根据绝对多数投票法决定最终分类结果.实验结果表明,该方法可以较好地进行数据分类,而且与传统的神经网络方法以及贝叶斯方法和决策树方法相比,在     

基于增量加权的不平衡漂移数据流分类算法

概念漂移是数据流学习领域中的一个难点问题,同时数据流中存在的类不平衡问题也会严重影响算法的分类性能。针对概念漂移和类不平衡的联合问题,在基于数据块集成的方法上引入在线更新机制,结合重采样和遗忘机制提出了一种增量加权集成的不平衡数据流分类方法(incremental weighted ensemble for imbalance learning, IWEIL)。该方法以集成框架为基础,利用基于可变大小窗口的遗忘机制确定基分类器对窗口内最近若干实例的分类性能,并计算基分类器的权重,随着新实例的逐个到达,在线更新IWEIL中每个基分器及其权重。同时,使用改进的自适应最近邻SMOTE方法生成符合新概念的新少数类实例以解决数据流中类不平衡问题。在人工数据集和真实数据集上进行实验,结果表明,相比于DWMIL算法,IWEIL在HyperPlane数据集上的G-mean和recall指标分别提升了5.77%和6.28%,在Electricity数据集上两个指标分别提升了3.25%和6.47%。最后,IWEIL在安卓应用检测问题上表现良好。     

Non‐negative sparse‐based SemiBoost for software defect prediction

Software defect prediction is an important decision support activity in software quality assurance. The limitation of the labelled modules usually makes the prediction difficult, and the class‐imbalance characteristic of software defect data leads to negative influence on decision of classifiers. Semi‐supervised learning can build high‐performance classifiers by using large amount of unlabelled modules together with the labelled modules. Ensemble learning achieves a better prediction capability for class‐imbalance data by using a series of weak classifiers to reduce the bias generated by the majority class. In this paper, we propose a new semi‐supervised software defect prediction approach, non‐negative sparse‐based SemiBoost learning. The approach is capable of exploiting both labelled and unlabelled data and is formulated in a boosting framework. In order to enhance the prediction ability, we design a flexible non‐negative sparse similarity matrix, which can fully exploit the similarity of historical data by incorporating the non‐negativity constraint into sparse learning for better learning the latent clustering relationship among software modules. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that non‐negative sparse‐based SemiBoost learning outperforms several representative state‐of‐the‐art semi‐supervised software defect prediction methods. Copyright © 2016 John Wiley & Sons, Ltd.     

属性加权的朴素贝叶斯集成分类器

为提高朴素贝叶斯分类器的分类精度和泛化能力,提出了基于属性相关性的加权贝叶斯集成方法（WEBNC）。根据每个条件属性与决策属性的相关度对其赋以相应的权值,然后用AdaBoost训练属性加权后的BNC。该分类方法在16个UCI标准数据集上进行了测试,并与BNC、贝叶斯网和由AdaBoost训练出的BNC进行比较,实验结果表明,该分类器具有更高的分类精度与泛化能力。     

基于准确率爬坡的动态加权集成分类算法

传统集成分类算法中,一般将集成数目设置为固定值,这可能会导致较低分类准确率。针对这一问题,提出了准确率爬坡集成分类算法（C-ECA）。首先,该算法不再用一些基分类器去替换相同数量的表现最差的基分类器,而是基于准确率对基分类器进行更新,然后确定最佳集成数目。其次,在C-ECA的基础上提出了基于爬坡的动态加权集成分类算法（C-DWECA）。该算法提出了一个加权函数,其在具有不同特征的数据流上训练基分类器时,可以获得基分类器的最佳权值,从而提升集成分类器的性能。最后,为了能更早地检测到概念漂移并提高最终精度,采用了快速霍夫丁漂移检测方法（FHDDM）。实验结果表明C-DWECA的准确率最高可达到97.44%,并且该算法的平均准确率比自适应多样性的在线增强（ADOB）算法提升了40%左右,也优于杠杆装袋（LevBag）、自适应随机森林（ARF）等其他对比算法。     

Classification using Hierarchical Naïve Bayes models

Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to “information double-counting” and interaction omission. In this paper we focus on a relatively new set of models, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models in the context of classification. Experimental results show that the learned models can significantly improve classification accuracy as compared to other frameworks.     

A Gaussian mixture model based discretization algorithm for associative classification of medical data

Knowledge-based systems such as expert systems are of particular interest in medical applications as extracted if-then rules can provide interpretable results. Various rule induction algorithms have been proposed to effectively extract knowledge from data, and they can be combined with classification methods to form rule-based classifiers. However, most of the rule-based classifiers can not directly handle numerical data such as blood pressure. A data preprocessing step called discretization is required to convert such numerical data into a categorical format. Existing discretization algorithms do not take into account the multimodal class densities of numerical variables in datasets, which may degrade the performance of rule-based classifiers. In this paper, a new Gaussian Mixture Model based Discretization Algorithm (GMBD) is proposed that preserve the most frequent patterns of the original dataset by taking into account the multimodal distribution of the numerical variables. The effectiveness of GMBD algorithm was verified using six publicly available medical datasets. According to the experimental results, the GMBD algorithm outperformed five other static discretization methods in terms of the number of generated rules and classification accuracy in the associative classification algorithm. Consequently, our proposed approach has a potential to enhance the performance of rule-based classifiers used in clinical expert systems.     

Intelligent GPGPU Classification in Volume Visualization: A framework based on Error‐Correcting Output Codes

In volume visualization, the definition of the regions of interest is inherently an iterative trial‐and‐error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi‐dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on‐demand multiple regions of interest. These methods can be split into a two‐level GPU‐based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error‐Correcting Output Codes (ECOC) framework. In a pre‐processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre‐labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi‐class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU‐OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.     

Semantic segmentation based on fusion of features and classifiers

This paper proposes a feed forward architecture algorithm using fusion of features and classifiers for semantic segmentation. The algorithm consists of three phases: Firstly, the features from hierarchical convolutional neural network (CNN) and the features based on region are extracted and fused on super pixel level; secondly, multiple classifiers of Softmax, XGBoost and Random Forest are ensemble to compute the per-pixel class probabilities; at last, a fully connected conditional random field is employed to enhance the final performance. The hierarchical features contain more global evidence and the region features contain more local evidence. So the fusion of these two features is expected to enhance the feature representation ability. In classification phase, integrating multiple classifiers aims to improve the generalization ability of classification algorithms. Experiments are conducted on Sift-Flow datasets by our proposed methods with competitive labeling accuracy.

     

Double-layer bayesian classifier ensembles based on frequent itemsets

Numerous models have been proposed to reduce the classification error of Na¨ ve Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classification error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles (DLBCE) algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier (DLBC) for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.     

基于分解策略处理多分类不均衡问题的方法

针对多分类不均衡问题,提出了一种新的基于一对一（one-versus-one,OVO）分解策略的方法。首先基于OVO分解策略将多分类不均衡问题分解成多个二值分类问题;再利用处理不均衡二值分类问题的算法建立二值分类器;接着利用SMOTE过抽样技术处理原始数据集;然后采用基于距离相对竞争力加权方法处理冗余分类器;最后通过加权投票法获得输出结果。在KEEL不均衡数据集上的大量实验结果表明,所提算法比其他经典方法具有显著的优势。     

Feature selection in single and ensemble learning‐based bankruptcy prediction models

Feature selection is an important data preprocessing step for the construction of an effective bankruptcy prediction model. The prediction performance can be affected by the employed feature selection and classification techniques. However, there have been very few studies of bankruptcy prediction that identify the best combination of feature selection and classification techniques. In this study, two types of feature selection methods, including filter‐ and wrapper‐based methods, are considered, and two types of classification techniques, including statistical and machine learning techniques, are employed in the development of the prediction methods. In addition, bagging and boosting ensemble classifiers are also constructed for comparison. The experimental results based on three related datasets that contain different numbers of input features show that the genetic algorithm as the wrapper‐based feature selection method performs better than the filter‐based one by information gain. It is also shown that the lowest prediction error rates for the three datasets are provided by combining the genetic algorithm with the naïve Bayes and support vector machine classifiers without bagging and boosting.     

基于代表的留一法集成学习分类

为应对抽样不均匀带来的影响,以基于代表的分类算法为基础,提出一种用于符号型数据分类的留一法集成学习分类算法（LOOELCA）。首先采用留一法获得n个小训练集,其中n为初始训练集大小。然后使用每个训练集构建独立的基于代表的分类器,并标注出分类错误的分类器及对象。最后,标注分类器和原始分类器形成委员会并对测试集对象进行分类。如委员会表决一致,则直接给该测试对象贴上类标签;否则,基于k最近邻（kNN）算法并利用标注对象对测试对象分类。在UCI标准数据集上的实验结果表明,LOOELCA与基于代表的粗糙集覆盖分类（RBC-CBNRS）算法相比,精度平均提升0.35~2.76个百分点,LOOELCA与ID3、J48、Naïve Bayes、OneR等方法相比也有更高的分类准确率。     

Improved classification with allocation method and multiple classifiers

Classification is the most used supervized machine learning method. As each of the many existing classification algorithms can perform poorly on some data, different attempts have arisen to improve the original algorithms by combining them. Some of the best know results are produced by ensemble methods, like bagging or boosting. We developed a new ensemble method called allocation. Allocation method uses the allocator, an algorithm that separates the data instances based on anomaly detection and allocates them to one of the micro classifiers, built with the existing classification algorithms on a subset of training data. The outputs of micro classifiers are then fused together into one final classification. Our goal was to improve the results of original classifiers with this new allocation method and to compare the classification results with existing ensemble methods. The allocation method was tested on 30 benchmark datasets and was used with six well known basic classification algorithms (J48, NaiveBayes, IBk, SMO, OneR and NBTree). The obtained results were compared to those of the basic classifiers as well as other ensemble methods (bagging, MultiBoost and AdaBoost). Results show that our allocation method is superior to basic classifiers and also to tested ensembles in classification accuracy and f-score. The conducted statistical analysis, when all of the used classification algorithms are considered, confirmed that our allocation method performs significantly better both in classification accuracy and f-score. Although the differences are not significant for each of the used basic classifier alone, the allocation method achieved the biggest improvements on all six basic classification algorithms. In this manner, allocation method proved to be a competitive ensemble method for classification that can be used with various classification algorithms and can possibly outperform other ensembles on different types of data.     

EVALUATION OF CLASSIFIERS FOR AN UNEVEN CLASS DISTRIBUTION PROBLEM

Classification problems with uneven class distributions present several difficulties during the training as well as during the evaluation process of classifiers. A classification problem with such characteristics has resulted from a data mining project where the objective was to predict customer insolvency. Using the data set from the customer insolvency problem, we study several alternative methodologies, which have been reported to better suit the specific characteristics of this type of problem. Three different but equally important directions are examined: (a) the performance measures that should be used for problems in this domain; (b) the class distributions that should be used for the training data sets; and (c) the classification algorithms to be used. The final evaluation of the resulting classifiers is based on a study of the economic impact of classification results. This study concludes to a framework that provides the “best” classifiers, identifies the performance measures that should be used as the decision criterion, and suggests the “best” class distribution based on the value of the relative gain from correct classification in the positive class. This framework has been applied in the customer insolvency problem, but it is claimed that it can be applied to many similar problems with uneven class distributions that almost always require a multi-objective evaluation process.     

Ensemble classification based on supervised clustering for credit scoring

Credit scoring aims to assess the risk associated with lending to individual consumers. Recently, ensemble classification methodology has become popular in this field. However, most researches utilize random sampling to generate training subsets for constructing the base classifiers. Therefore, their diversity is not guaranteed, which may lead to a degradation of overall classification performance. In this paper, we propose an ensemble classification approach based on supervised clustering for credit scoring. In the proposed approach, supervised clustering is employed to partition the data samples of each class into a number of clusters. Clusters from different classes are then pairwise combined to form a number of training subsets. In each training subset, a specific base classifier is constructed. For a sample whose class label needs to be predicted, the outputs of these base classifiers are combined by weighted voting. The weight associated with a base classifier is determined by its classification performance in the neighborhood of the sample. In the experimental study, two benchmark credit data sets are adopted for performance evaluation, and an industrial case study is conducted. The results show that compared to other ensemble classification methods, the proposed approach is able to generate base classifiers with higher diversity and local accuracy, and improve the accuracy of credit scoring.     

Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

In the class imbalanced learning scenario, traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested. To solve this problem, many effective approaches have been proposed. Among them, the bagging ensemble methods with integration of the under-sampling techniques have demonstrated better performance than some other ones including the bagging ensemble methods integrated with the over-sampling techniques, the cost-sensitive methods, etc. Although these under-sampling techniques promote the diversity among the generated base classifiers with the help of random partition or sampling for the majority class, they do not take any measure to ensure the individual classification performance, consequently affecting the achievability of better ensemble performance. On the other hand, evolutionary under-sampling EUS as a novel undersampling technique has been successfully applied in searching for the best majority class subset for training a good-performance nearest neighbor classifier. Inspired by EUS, in this paper, we try to introduce it into the under-sampling bagging framework and propose an EUS based bagging ensemble method EUS-Bag by designing a new fitness function considering three factors to make EUS better suited to the framework. With our fitness function, EUS-Bag could generate a set of accurate and diverse base classifiers. To verify the effectiveness of EUS-Bag, we conduct a series of comparison experiments on 22 two-class imbalanced classification problems. Experimental results measured using recall, geometric mean and AUC all demonstrate its superior performance.     

基于拆分集成的不均衡数据分类算法

为改进SVM对不均衡数据的分类性能,提出一种基于拆分集成的不均衡数据分类算法,该算法对多数类样本依据类别之间的比例通过聚类划分为多个子集,各子集分别与少数类合并成多个训练子集,通过对各训练子集进行学习获得多个分类器,利用WE集成分类器方法对多个分类器进行集成,获得最终分类器,以此改进在不均衡数据下的分类性能.在UCI数据集上的实验结果表明,该算法的有效性,特别是对少数类样本的分类性能.     

An assessment of the effectiveness of decision tree methods for land cover classification

Choice of a classification algorithm is generally based upon a number of factors, among which are availability of software, ease of use, and performance, measured here by overall classification accuracy. The maximum likelihood (ML) procedure is, for many users, the algorithm of choice because of its ready availability and the fact that it does not require an extended training process. Artificial neural networks (ANNs) are now widely used by researchers, but their operational applications are hindered by the need for the user to specify the configuration of the network architecture and to provide values for a number of parameters, both of which affect performance. The ANN also requires an extended training phase.In the past few years, the use of decision trees (DTs) to classify remotely sensed data has increased. Proponents of the method claim that it has a number of advantages over the ML and ANN algorithms. The DT is computationally fast, make no statistical assumptions, and can handle data that are represented on different measurement scales. Software to implement DTs is readily available over the Internet. Pruning of DTs can make them smaller and more easily interpretable, while the use of boosting techniques can improve performance.In this study, separate test and training data sets from two different geographical areas and two different sensors—multispectral Landsat ETM+ and hyperspectral DAIS—are used to evaluate the performance of univariate and multivariate DTs for land cover classification. Factors considered are: the effects of variations in training data set size and of the dimensionality of the feature space, together with the impact of boosting, attribute selection measures, and pruning. The level of classification accuracy achieved by the DT is compared to results from back-propagating ANN and the ML classifiers. Our results indicate that the performance of the univariate DT is acceptably good in comparison with that of other classifiers, except with high-dimensional data. Classification accuracy increases linearly with training data set size to a limit of 300 pixels per class in this case. Multivariate DTs do not appear to perform better than univariate DTs. While boosting produces an increase in classification accuracy of between 3% and 6%, the use of attribute selection methods does not appear to be justified in terms of accuracy increases. However, neither the univariate DT nor the multivariate DT performed as well as the ANN or ML classifiers with high-dimensional data.     

An evidential reasoning based classification algorithm and its application for face recognition with class noise

For classification problems, in practice, real-world data may suffer from two types of noise, attribute noise and class noise. It is the key for improving recognition performance to remove as much of their adverse effects as possible. In this paper, a formalism algorithm is proposed for classification problems with class noise, which is more challenging than those with attribute noise. The proposed formalism algorithm is based on evidential reasoning theory which is a powerful tool to deal with uncertain information in multiple attribute decision analysis and many other areas. Thus, it may be more effective alternative to handle noisy label information. And then a specific algorithm—Evidential Reasoning based Classification algorithm (ERC) is derived to recognize human faces under class noise conditions. The proposed ERC algorithm is extensively evaluated on five publicly available face databases with class noise and yields good performance.