聚类集成中的差异性度量研究

集体的差异性被认为是影响集成学习的一个关键因素.在分类器集成中有许多的差异性度量被提出,但是在聚类集成中如何测量聚类集体的差异性,目前研究得很少.作者研究了7种聚类集体差异性度量方法,并通过实验研究了这7种度量在不同的平均成员聚类准确度、不同的集体大小和不同的数据分布情况下与各种聚类集成算法性能之间的关系.实验表明:这些差异性度量与聚类集成性能间并没有单调关系,但是在平均成员准确度较高、聚类集体大小适中和数据中有均匀簇分布的情况下,它们与集成性能间的相关度还是比较高的.最后给出了一些差异性度量用于指导聚类集体生成的可行性建议.     

选择性聚类融合研究进展

传统的聚类融合方法通常是将所有产生的聚类成员融合以获得最终的聚类结果。在监督学习中,选择分类融合方法会获得更好的结果,从选择分类融合中得到启示,在聚类融合中应用这种方法被定义为选择性聚类融合。对选择性聚类融合关键技术进行了综述,讨论了未来的研究方向。     

Selection of Heterogeneous Fuzzy Model Ensembles Using Self-adaptive Genetic Algorithms

The problem of model selection to compose a heterogeneous bagging ensemble was addressed in the paper. To solve the problem, three self-adapting genetic algorithms were proposed with different control parameters of mutation, crossover, and selection adjusted during the execution. The algorithms were applied to create heterogeneous ensembles comprising regression fuzzy models to aid in real estate appraisals. The results of experiments revealed that the self-adaptive algorithms converged faster than the classic genetic algorithms. The heterogeneous ensembles created by self-adapting methods showed a very good predictive accuracy when compared with the homogeneous ensembles obtained in earlier research.     

Effects of resampling method and adaptation on clustering ensemble efficacy

Clustering ensembles combine multiple partitions of data into a single clustering solution of better quality. Inspired by the success of supervised bagging and boosting algorithms, we propose non-adaptive and adaptive resampling schemes for the integration of multiple independent and dependent clusterings. We investigate the effectiveness of bagging techniques, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms. In our adaptive approach, individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given dataset. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are then drawn to increasingly focus on the problematic regions of the input feature space. A measure of data point clustering consistency is therefore defined to guide this adaptation. Experimental results show improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and adaptive techniques. A meaningful consensus partition for an entire set of data points emerges from multiple clusterings of bootstraps and subsamples. Subsamples of small size can reduce computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical study also compares the performance of adaptive and non-adaptive clustering ensembles using different consensus functions on a number of datasets. By focusing attention on the data points with the least consistent clustering assignments, whether one can better approximate the inter-cluster boundaries or can at least create diversity in boundaries and this results in improving clustering accuracy and convergence speed as a function of the number of partitions in the ensemble. The comparison of adaptive and non-adaptive approaches is a new avenue for research, and this study helps to pave the way for the useful application of distributed data mining methods.     

Weighted association based methods for the combination of heterogeneous partitions

Co-association matrix has been a useful tool in many clustering ensemble techniques as a similarity measure between objects. In this paper, we introduce the weighted-association matrix, which is more expressive than the traditional co-association as a similarity measure, in the sense that it integrates information from the set of partitions in the clustering ensemble as well as from the original data of object representations. The weighted-association matrix is the core of the two main contributions of this paper: a natural extension of the well-known evidence accumulation cluster ensemble method by using the weighted-association matrix and a kernel based clustering ensemble method that uses a new data representation. These methods are compared with simple clustering algorithms as well as with other clustering ensemble algorithms on several datasets. The obtained results ratify the accuracy of the proposed algorithms.     

层次聚类的簇集成方法研究

聚类集成比单个聚类方法具有更高的鲁棒性和精确性,它主要由两部分组成,即个体成员的产生和结果的融合。针对聚类集成,首先用k-means聚类算法得到个体成员,然后使用层次聚类中的单连接法、全连接法与平均连接法进行融合。为了评价聚类集成方法的性能,实验中使用了ARI（Adjusted Rand Index）。实验结果表明,平均连接法的聚类集成性能优于单连接法和全连接法。研究并讨论了融合方法的聚类正确率和集成规模的关系。     

Evaluation of stability of k-means cluster ensembles with respect to random initialization

Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results     

A fine-grained Random Forests using class decomposition: an application to medical diagnosis

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable data set without the need for feature engineering required by techniques like support vector machines and deep learning. For ensembles, the decomposition is a natural way to increase diversity, a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of data sets that are mainly related to the medical domain. Results reported in this paper show clearly that our method has significantly improved the accuracy of Random Forests.     

一种改进的多视图聚类集成算法

近年来,针对大数据的数据挖掘技术和机器学习算法研究变得日趋重要。在聚类领域,随着多视图数据的大量出现,多视图聚类已经成为了一类重要的聚类方法。然而,大多数现有的多视图聚类算法受算法参数设置、数据样本等影响,具有聚类结果不稳定、参数需要反复调节等缺点。基于多视图K-means算法和聚类集成技术,提出了一种改进的多视图聚类集成算法,其提高了聚类的准确性、鲁棒性和稳定性。其次,由于单机环境下的多视图聚类算法难以对海量的数据进行处理,结合分布式处理技术,实现了一种分布式的多视图并行聚类算法。实验证明,并行算法在处理大数据时的时间效率有很大提升,适合于大数据环境下的多视图聚类分析。     

Behavioral Diversity and a Probabilistically Optimal GP Ensemble

We propose N-version Genetic Programming (NVGP) as an ensemble method to enhance accuracy and reduce performance fluctuation of programs produced by genetic programming. Diversity is essential for forming successful ensembles. NVGP quantifies behavioral diversity of ensemble members and defines NVGP optimal as an ensemble that has independent fault occurrences among its members. We observed significant accuracy improvement by NVGP optimal ensembles when applied to a DNA segment classification problem.     

Overfitting cautious selection of classifier ensembles with genetic algorithms

Information fusion research has recently focused on the characteristics of the decision profiles of ensemble members in order to optimize performance. These characteristics are particularly important in the selection of ensemble members. However, even though the control of overfitting is a challenge in machine learning problems, much less work has been devoted to the control of overfitting in selection tasks. The objectives of this paper are: (1) to show that overfitting can be detected at the selection stage; and (2) to present strategies to control overfitting. Decision trees and k nearest neighbors classifiers are used to create homogeneous ensembles, while single- and multi-objective genetic algorithms are employed as search algorithms at the selection stage. In this study, we use bagging and random subspace methods for ensemble generation. The classification error rate and a set of diversity measures are applied as search criteria. We show experimentally that the selection of classifier ensembles conducted by genetic algorithms is prone to overfitting, especially in the multi-objective case. In this study, the partial validation, backwarding and global validation strategies are tailored for classifier ensemble selection problem and compared. This comparison allows us to show that a global validation strategy should be applied to control overfitting in pattern recognition systems involving an ensemble member selection task. Furthermore, this study has helped us to establish that the global validation strategy can be used to measure the relationship between diversity and classification performance when diversity measures are employed as single-objective functions.     

一种基于投票策略的聚类融合算法

在分类算法和回归模型中,融合方法正得到越来越广泛的应用,但在非监督机器学习领域,由于缺乏数据集的先验知识,则不能直接用于聚类算法.提出并实现了一种基于投票策略的聚类融合算法,该算法利用k-means算法每次随机选取聚类中心而得到不同样本划分的特性,将多次运行得到的聚类结果通过投票的方式合并,从而得到最终的结果.通过一系列真实数据和合成数据集的实验证明,这种方法比单一的聚类算法能更有效地提高聚类的准确率.在此基础上,为了降低高维数据运算的复杂性,将随机划分属性子空间的方法应用到上述聚类融合算法中,实验证明,该方法同时也能够在一个属性子空间上获得好的聚类结果.     

Co-evolutionary multi-population genetic programming for classification in software defect prediction: An empirical case study

Evolving diverse ensembles using genetic programming has recently been proposed for classification problems with unbalanced data. Population diversity is crucial for evolving effective algorithms. Multilevel selection strategies that involve additional colonization and migration operations have shown better performance in some applications. Therefore, in this paper, we are interested in analysing the performance of evolving diverse ensembles using genetic programming for software defect prediction with unbalanced data by using different selection strategies. We use colonization and migration operators along with three ensemble selection strategies for the multi-objective evolutionary algorithm. We compare the performance of the operators for software defect prediction datasets with varying levels of data imbalance. Moreover, to generalize the results, gain a broader view and understand the underlying effects, we replicated the same experiments on UCI datasets, which are often used in the evolutionary computing community. The use of multilevel selection strategies provides reliable results with relatively fast convergence speeds and outperforms the other evolutionary algorithms that are often used in this research area and investigated in this paper. This paper also presented a promising ensemble strategy based on a simple convex hull approach and at the same time it raised the question whether ensemble strategy based on the whole population should also be investigated.     

Cluster ensembles: A survey of approaches with recent extensions and applications

Cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across different data collections. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the corresponding parameters, given a set of data to be investigated. Almost two decades after the first publication of a kind, the method has proven effective for many problem domains, especially microarray data analysis and its down-streaming applications. Recently, it has been greatly extended both in terms of theoretical modelling and deployment to problem solving. The survey attempts to match this emerging attention with the provision of fundamental basis and theoretical details of state-of-the-art methods found in the present literature. It yields the ranges of ensemble generation strategies, summarization and representation of ensemble members, as well as the topic of consensus clustering. This review also includes different applications and extensions of cluster ensemble, with several research issues and challenges being highlighted.     

What influences the accuracy of decision tree ensembles?

An ensemble in machine learning is defined as a set of models (such as classifiers or predictors) that are induced individually from data by using one or more machine learning algorithms for a given task and then work collectively in the hope of generating improved decisions. In this paper we investigate the factors that influence ensemble performance, which mainly include accuracy of individual classifiers, diversity between classifiers, the number of classifiers in an ensemble and the decision fusion strategy. Among them, diversity is believed to be a key factor but more complex and difficult to be measured quantitatively, and it was thus chosen as the focus of this study, together with the relationships between the other factors. A technique was devised to build ensembles with decision trees that are induced with randomly selected features. Three sets of experiments were performed using 12 benchmark datasets, and the results indicate that (i) a high level of diversity indeed makes an ensemble more accurate and robust compared with individual models; (ii) small ensembles can produce results as good as, or better than, large ensembles provided the appropriate (e.g. more diverse) models are selected for the inclusion. This has implications that for scaling up to larger databases the increased efficiency of smaller ensembles becomes more significant and beneficial. As a test case study, ensembles are built based on these findings for a real world application—osteoporosis classification, and found that, in each case of three datasets used, the ensembles out-performed individual decision trees consistently and reliably.     

基于个体选择的动态权重神经网络集成方法研究

神经网络集成技术能有效地提高神经网络的预测精度和泛化能力,已成为机器学习和神经计算领域的一个研究热点。该文针对回归分析问题提出了一种结合应用遗传算法进行个体选择和动态确定结果合成权重的神经网络集成构造方法。在训练出个体神经网络之后,应用遗传算法对个体网络进行选择,然后根据被选择的各个体网络在输入空间上对训练样本的预测误差,应用广义回归网络来动态地确定各个体网络在特定输入空间上的合成权重。实验结果表明,与仅应用个体网络选择或动态确定权重的方法相比,该集成方法基本上能取得更好地预测精度和相近的稳定性。     

Improvements in image categorization using codebook ensembles

The problem of object category classification by committees or ensembles of classifiers, each of which is based on one diverse codebook, is addressed in this paper. Two methods of constructing visual codebook ensembles are proposed in this study. The first technique introduces diverse individual visual codebooks using different clustering algorithms. The second uses various visual codebooks of different sizes for constructing an ensemble with high diversity. Codebook ensembles are trained to capture and convey image properties from different aspects. Based on these codebook ensembles, different types of image representations can be acquired. A classifier ensemble can be trained based on different expression datasets from the same training image set. The use of a classifier ensemble to categorize new images can lead to improved performance. Detailed experimental analysis on a Pascal VOC challenge dataset reveals that the present ensemble approach performs well, consistently improves the performance of visual object classifiers, and results in state-of-the-art performance in categorization.     

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.     

A fast ensemble pruning algorithm based on pattern mining process

Ensemble pruning deals with the reduction of base classifiers prior to combination in order to improve generalization and prediction efficiency. Existing ensemble pruning algorithms require much pruning time. This paper presents a fast pruning approach: pattern mining based ensemble pruning (PMEP). In this algorithm, the prediction results of all base classifiers are organized as a transaction database, and FP-Tree structure is used to compact the prediction results. Then a greedy pattern mining method is explored to find the ensemble of size k. After obtaining the ensembles of all possible sizes, the one with the best accuracy is outputted. Compared with Bagging, GASEN, and Forward Selection, experimental results show that PMEP achieves the best prediction accuracy and keeps the size of the final ensemble small, more importantly, its pruning time is much less than other ensemble pruning algorithms.     

Cluster ensembles in collaborative filtering recommendation

Recommender systems, which recommend items of information that are likely to be of interest to the users, and filter out less favored data items, have been developed. Collaborative filtering is a widely used recommendation technique. It is based on the assumption that people who share the same preferences on some items tend to share the same preferences on other items. Clustering techniques are commonly used for collaborative filtering recommendation. While cluster ensembles have been shown to outperform many single clustering techniques in the literature, the performance of cluster ensembles for recommendation has not been fully examined. Thus, the aim of this paper is to assess the applicability of cluster ensembles to collaborative filtering recommendation. In particular, two well-known clustering techniques (self-organizing maps (SOM) and k-means), and three ensemble methods (the cluster-based similarity partitioning algorithm (CSPA), hypergraph partitioning algorithm (HGPA), and majority voting) are used. The experimental results based on the Movielens dataset show that cluster ensembles can provide better recommendation performance than single clustering techniques in terms of recommendation accuracy and precision. In addition, there are no statistically significant differences between either the three SOM ensembles or the three k-means ensembles. Either the SOM or k-means ensembles could be considered in the future as the baseline collaborative filtering technique.