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This paper advocates a problem-oriented approach for the design of artificial immune systems (AIS) for data mining. By problem-oriented approach we mean that, in real-world data mining applications the design of an AIS should take into account the characteristics of the data to be mined together with the application domain: the components of the AIS—such as its representation, affinity function, and immune process—should be tailored for the data and the application. This is in contrast with the majority of the literature, where a very generic AIS algorithm for data mining is developed and there is little or no concern in tailoring the components of the AIS for the data to be mined or the application domain. To support this problem-oriented approach, we provide an extensive critical review of the current literature on AIS for data mining, focusing on the data mining tasks of classification and anomaly detection. We discuss several important lessons to be taken from the natural immune system to design new AIS that are considerably more adaptive than current AIS. Finally, we conclude this paper with a summary of seven limitations of current AIS for data mining and ten suggested research directions. [ABSTRACT FROM AUTHOR]

k nearest neighbor (k NN) is a simple and widely used classifier; it can achieve comparable performance with more complex classifiers including decision tree and artificial neural network. Therefore, k NN has been listed as one of the top 10 algorithms in machine learning and data mining. On the other hand, in many classification problems, such as medical diagnosis and intrusion detection, the collected training sets are usually class imbalanced. In class imbalanced data, although positive examples are heavily outnumbered by negative ones, positive examples usually carry more meaningful information and are more important than negative examples. Similar to other classical classifiers, k NN is also proposed under the assumption that the training set has approximately balanced class distribution, leading to its unsatisfactory performance on imbalanced data. In addition, under a class imbalanced scenario, the global resampling strategies that are suitable to decision tree and artificial neural network often do not work well for k NN, which is a local information-oriented classifier. To solve this problem, researchers have conducted many works for k NN over the past decade. This paper presents a comprehensive survey of these works according to their different perspectives and analyzes and compares their characteristics. At last, several future directions are pointed out. [ABSTRACT FROM AUTHOR]

In this paper, a recurrent wavelet-based neuro-fuzzy identifier (RWNFI) with a self-organization hybrid evolution learning algorithm (SOHELA) is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution (GSE) is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm (SOA) to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method (DMSM) and the data mining-based crossover method (DMCM) to determine groups and parent groups using the data mining method called the frequent pattern growth (FP-Growth) method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models. [ABSTRACT FROM AUTHOR]

The current data dynamic migration algorithm ignores the attribute characteristics of data in the process of data layout, which leads to more iterations of data in perceptual virtual network, longer downtime of dynamic migration and lower migration efficiency. To solve this problem, a dynamic migration algorithm of perceptual data in virtual network based on machine learning is proposed. Through machine learning algorithm to mine the attribute characteristics of virtual network perception data, Moran's I index is obtained to analyze the correlation index of perception data. By calculating the spatial location of data perception, the data center with less workload in virtual network is selected, and the data center of each data center is calculated. By determining the target node, selecting the migration sensing data and setting the migration factor as the limiting condition, the dynamic migration of sensing data is realized. Experimental results show that the proposed algorithm can effectively reduce the number of iterative replication rounds, shorten the downtime of dynamic migration, and improve the efficiency of virtual network migration in the environment of high dirty page rate and low dirty page rate. [ABSTRACT FROM AUTHOR]

Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification”. In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of the cost-sensitive measures made by the proposed algorithms, and extensively examine their empirical performance on a variety of cost-sensitive online classification tasks. Finally, we demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be a highly efficient and effective tool to tackle cost-sensitive online classification tasks in various application domains. [ABSTRACT FROM AUTHOR]

Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and optimization methods, have become powerful tools for problem solution in machine learning. SVMs reduce most machine learning problems to optimization problems and optimization lies at the heart of SVMs. Lots of SVM algorithms involve solving not only convex problems, such as linear programming, quadratic programming, second order cone programming, semi-definite programming, but also non-convex and more general optimization problems, such as integer programming, semi-infinite programming, bi-level programming and so on. The purpose of this paper is to understand SVM from the optimization point of view, review several representative optimization models in SVMs, their applications in economics, in order to promote the research interests in both optimization-based SVMs theory and economics applications. This paper starts with summarizing and explaining the nature of SVMs. It then proceeds to discuss optimization models for SVM following three major themes. First, least squares SVM, twin SVM, AUC Maximizing SVM, and fuzzy SVM are discussed for standard problems. Second, support vector ordinal machine, semisupervised SVM, Universum SVM, robust SVM, knowledge based SVM and multi-instance SVM are then presented for nonstandard problems. Third, we explore other important issues such as lp-norm SVM for feature selection, LOOSVM based on minimizing LOO error bound, probabilistic outputs for SVM, and rule extraction from SVM. At last, several applications of SVMs to financial forecasting, bankruptcy prediction, credit risk analysis are introduced. [ABSTRACT FROM AUTHOR]

Simulated Annealing (SA) has been adopted by many Ensemble Clustering methods to achieve global combinational optimisation. However the performance of SA is sensitive to the settings of its parameters. Much work has been done for optimising the settings of these parameters over the last two decades, but few of them analysed the behaviour of different cooling functions for Ensemble Clustering. Our work has demonstrated that the clustering results could be invalid if we use SA for Ensemble Clustering without a good understanding of the behaviour of cooling functions. Therefore this paper aims to present the findings of how different cooling functions may affect the performance of Ensemble Clustering methods that use SA. We analyse the effect of cooling functions from three aspects: the convergence rate, the final value of the objective function, and the accuracy of results. Ten different cooling functions are tested on two Ensemble Clustering methods, and thirteen different datasets have been used for the experiments. The findings are particularly helpful for those who are interested in Ensemble Clustering methods as well as those who want to obtain a deep understanding of the behaviour of the cooling functions. [ABSTRACT FROM AUTHOR]

Incremental Learning on non stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, an advertisement recommendation system, in which customer's behavior may change depending on the season of the year, on the inflation and on new products made available. An extra challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the training data is balanced. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream (ENSDS) with focus on two-class problems. In addition, we are presenting here an exhaustive comparison of purposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, fmeasure and g-mean. [ABSTRACT FROM AUTHOR]

Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results. [ABSTRACT FROM PUBLISHER]

Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides, the existing methods are likely to generate noisy instances. To solve these problems, this paper proposes a deep instance envelope network-based imbalanced learning algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This algorithm can guarantee high quality balanced instances using a deep instance envelope network in the absence of prior knowledge. First, the MlFCM is designed for the original minority class instances to obtain deep instances and increase the diversity of instances. Then, the MIDMD is proposed to avoid the generation of noisy instances and maintain the consistency of the interlayers of instances. Next, the multilayer FCM and minimum interlayer discrepancy mechanism are combined to construct a deep instance envelope network – the MlFC&IDMD. Finally, an imbalance learning algorithm is proposed based on the MlFC&IDMD. In the experimental section, thirty-three popular public datasets are used for verification, and over ten representative algorithms are used for comparison. The experimental results show that the proposed approach significantly outperforms other popular methods. • For the first time, a multilayer FCM (MlFCM) algorithm for classification is proposed to mine the instances for more information. • A minimum interlayer discrepancy mechanism (MIDMD) is proposed to make the distribution of instances before and after clustering consistent. • A deep instance envelope network (MlFC&IDMD) is constructed by combining the MlFCM and MIDMD. • An efficient strategy is proposed to determine the number of layers L of the deep instance envelope network. • A new imbalance learning algorithm is proposed based on the MlFC&IDMD. [ABSTRACT FROM AUTHOR]

Instance selection is one of the most important preprocessing steps in many machine learning tasks. Due to the increasing size of the problems, removing useless, erroneous or noisy instances is frequently an initial step that is performed before other data mining algorithms are applied. Instance selection as part of this data reduction task is one of the most relevant problems in current data mining research. Over the past decades, many different instance selection algorithms have been proposed, each with its own strengths and weaknesses. However, as in the case of classification, it is unlikely that a single instance selection algorithm would be able to achieve good results across many different datasets and application fields. In classification, one of the most successful ways of consistently improving the performance of a single learner is the construction of ensembles using boosting methods. In this paper, we propose a novel approach for instance selection based on boosting instance selection algorithms in the same way boosting is applied to classification. The proposed approach opens a new field of research in which to apply the many techniques developed for boosting classifiers, for instance selection and other data reduction techniques such as feature selection and simultaneous instance and feature selection. Using 60 datasets for balanced problems and 45 datasets for class-imbalanced problems, the experiments reported show a clear improvement in several state-of-the-art instance selection algorithms using the proposed methodology. [ABSTRACT FROM AUTHOR]

Abstract: Clustering is a method of unsupervised learning, and a common technique for intelligent data analysis used in many fields. The enlarging volumes of information produced by the progress of technology, makes clustering of very large scale data a challenging task. Therefore, parallel processing of very large scale data set is a good solution to that problem. CLARANS is approved to be an efficient and effective clustering method for spatial data mining. It is insensitive to outliers and has the advantage of processing category data directly compared to other partition method like K-means. However, it is a serial algorithm and cannot process large scale data set efficiently. In this paper, we propose a parallel CLARANS clustering algorithm based on MapReduce, which is a current and powerful parallel programming framework. Experimental results demonstrate that the parallel CLARANS algorithm can handle large scale data set efficiently and effectively. Furthermore, the evaluation criterions of scaleup, speedup and sizeup validate its efficiency. [Copyright &y& Elsevier]

Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many areas of machine learning and data mining. During the past decade, numerous hashing algorithms are proposed to solve this problem. Every proposed algorithm claims to outperform Locality Sensitive Hashing (LSH), which is the most popular hashing method. However, the evaluation of these hashing article was not thorough enough, and the claim should be re-examined. If implemented correctly, almost all the hashing methods will have their performance improved as the code length increases. However, many existing hashing article only report the performance with the code length shorter than 128. In this article, we carefully revisit the problem of search-with-a-hash-index and analyze the pros and cons of two popular hash index search procedures. Then we proposed a simple but effective novel hash index search approach and made a thorough comparison of eleven popular hashing algorithms. Surprisingly, the random-projection-based Locality Sensitive Hashing ranked the first, which is in contradiction to the claims in all the other 10 hashing article. Despite the extreme simplicity of random-projection-based LSH, our results show that the capability of this algorithm has been far underestimated. For the sake of reproducibility, all the codes used in the article are released on GitHub, which can be used as a testing platform for a fair comparison between various hashing algorithms. [ABSTRACT FROM AUTHOR]