Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining. The existing algorithm recommendation models are generally constructed on only one kind of meta-features by single learners. Considering that i) ensemble learners usually show better performance and ii) different kinds of meta-features characterize the classification problems in different viewpoints independently, and further the models constructed with different sets of meta-features will be complementary with each other and applicable for ensemble. This paper proposes an ensemble learning-based algorithm recommendation method. To evaluate the proposed recommendation method, extensive experiments with 13 well-known candidate classification algorithms and five different kinds of meta-features are conducted on 1090 benchmark classification problems. The results show the effectiveness of the proposed ensemble learning based recommendation method.

Constraint based clustering that satisfies set of user-defined constraint. Constraint-based

Context: Software defect prediction (SDP) is an important challenge in the field of software engineering, hence much research work has been conducted, most notably through the use of machine learning algorithms. However, class-imbalance typified by few defective components and many non-defective ones is a common occurrence causing difficulties for these methods. Imbalanced learning aims to deal with this problem and has recently been deployed by some researchers, unfortunately with inconsistent results. Objective: We conduct a comprehensive experiment to explore (a) the basic characteristics of this problem; (b) the effect of imbalanced learning and its interactions with (i) data imbalance, (ii) type of classifier, (iii) input metrics and (iv) imbalanced learning method. Method: We systematically evaluate 27 data sets, 7 classifiers, 7 types of input metrics and 17 imbalanced learning methods (including doing nothing) using an experimental design that enables exploration of interactions between these factors and individual imbalanced learning algorithms. This yields 27 x7; 7 x7; 7 x7; 17 = 22491 results. The Matthews correlation coefficient (MCC) is used as an unbiased performance measure (unlike the more widely used F1 and AUC measures). Results: (a) we found a large majority (87 percent) of 106 public domain data sets exhibit moderate or low level of imbalance (imbalance ratio 003C;10; median = 3.94); (b) anything other than low levels of imbalance clearly harm the performance of traditional learning for SDP; (c) imbalanced learning is more effective on the data sets with moderate or higher imbalance, however negative results are always possible; (d) type of classifier has most impact on the improvement in classification performance followed by the imbalanced learning method itself. Type of input metrics is not influential. (e) only ${\sim} 52\%$similar to 52% of the combinations of Imbalanced Learner and Classifier have a significant positive effect. Conclusion: This paper offers two practical guidelines. First, imbalanced learning should only be considered for moderate or highly imbalanced SDP data sets. Second, the appropriate combination of imbalanced method and classifier needs to be carefully chosen to ameliorate the imbalanced learning problem for SDP. In contrast, the indiscriminate application of imbalanced learning can be harmful.

Traditional classification methods cannot well capture the characteristics of complex problems, thus leading to poor performance.In this paper, we propose a new framework named Partition based LAzy Classification (PLAC) to better characterize complex problems by dividing the training data space into smaller and easier-to-learn partitions.In PLAC, only the nearest partition of a new instance is used to train a local classifier that is finally used to classify the new instance.As the partitioning is performed based on information gain before receiving a new instance, the resulting partitions are groups of similar instances and the chance of the nearest instances of the new instance coming from different regions by accident is reduced.Moreover, our method uses only one partition to conduct a prediction and employs the caching mechanism to avoid work replication during classification, thus efficiency is improved.An extensive experimental evaluation on 40 real world data sets shows that PLAC effectively improves the performance of base classifiers and outperforms existing mainstream ensemble methods.

Unsupervised feature selection is an important problem, especially for high-dimensional data. However, until now, it has been scarcely studied and the existing algorithms cannot provide satisfying performance. Thus, in this paper, we propose a new unsupervised feature selection algorithm using similarity-based feature clustering, Feature Selection-based Feature Clustering (FSFC). FSFC removes redundant features according to the results of feature clustering based on feature similarity. First, it clusters the features according to their similarity. A new feature clustering algorithm is proposed, which overcomes the shortcomings of K-means. Second, it selects a representative feature from each cluster, which contains most interesting information of features in the cluster. The efficiency and effectiveness of FSFC are tested upon real-world data sets and compared with two representative unsupervised feature selection algorithms, Feature Selection Using Similarity (FSUS) and Multi-Cluster-based Feature Selection (MCFS) in terms of runtime, feature compression ratio, and the clustering results of K-means. The results show that FSFC can not only reduce the feature space in less time, but also significantly improve the clustering performance of K-means.

we propose a framework of combining Machine Learning with Dynamic Optimization for reconstructing scene in 3D automatically from a single still image of unstructured outdoor environment based on monocular vision of an uncalibrated camera. After segmenting image first time, a kind of searching tree strategy based on Bayes rule is used to identify the hierarchy of all areas on occlusion. After superpixel segmenting image second time, the AdaBoost algorithm is applied in the integration detection to the depth of lighting, texture and material. Finally, all the factors above are optimized with constrained conditions, acquiring the whole depthmap of an image. Integrate the source image with its depthmap in point-cloud or bilinear interpolation styles, realizing 3D reconstruction. Experiment in comparisons with typical methods in associated database demonstrates our method improves the reasonability of estimation to the overall 3D architecture of image's scene to a certain extent. And it does not need any manual assist and any camera model information.

An automatic recognition framework for human facial expressions from a monocular video with an uncalibrated camera is proposed. The expression characteristics are first acquired from a kind of deformable template, similar to a facial muscle distribution. After associated regularization, the time sequences from the trait changes in space-time under complete expressional production are then arranged line by line in a matrix. Next, the matrix dimensionality is reduced by a method of manifold learning of neighborhood-preserving embedding. Finally, the refined matrix containing the expression trait information is recognized by a classifier that integrates the hidden conditional random field (HCRF) and support vector machine (SVM). In an experiment using the Cohn–Kanade database, the proposed method showed a comparatively higher recognition rate than the individual HCRF or SVM methods in direct recognition from two-dimensional human face traits. Moreover, the proposed method was shown to be more robust than the typical Kotsia method because the former contains more structural characteristics of the data to be classified in space-time

Community detection is an important methodology for understanding the intrinsic structure and function of complex networks. Because overlapping community is one of the characteristics of real‐world networks and should be considered for community detection, in this article, we propose an algorithm, called link‐based label propagation algorithm (LinkLPA), to detect overlapping communities. Because the link partition is conceptually natural for the problem of overlapping community detection, LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm. Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over‐overlapping and incorrect partition of weak ties. Experimental results on a large number of real‐world and synthetic networks show that the proposed method achieves high accuracy on detecting overlapping communities in networks.

This paper presents a framework on multi-user and multi-view human eyes' detection and tracking. First, it uses fives kinds of AdaBoost face detectors with four different sizes at each area of image to detect faces in turn. Then, to locate eyes' positions, four kinds of AdaBoost eye detectors are used and if the eye-detection above fails, the prior knowledge of human organs' positions in anatomy is applied as a spare method. Next, it uses the unscented filter to predict the targets' next possible positions. Finally, the detection method above is used to detect the third frame and amend the relative forecasting. And repeat above cycle until tracking over. This framework is robust to subject's eyes' blinking, closing, wearing glasses and partly sheltering in multi-face and multi-view to a certain extent for the optimized structure performance and reasonable selected features. And because of the nonlinear filtering, it can track targets in curves with changing speeds. It mainly fits most usual vertical head scenes in monitoring environment.

Among many factors that influence the success of a software project, the software process model employed is an essential one. An improper process model will be time consuming, error-prone and cost expensive, and further lower the quality of software. Therefore, how to choose an appropriate software process model is a very important problem for software development. Current works focus on the selection criteria and often lead to subjective results. In this paper, we propose a software process model recommendation method, to help project managers choose the most appropriate software process model for a new project at an early stage of development process according to historical software engineering data. The proposed method casts the process model recommendation into a classification problem. It first evaluates the different combinations of the alternative classification and attribute selection algorithms, and the best one is used to build the recommendation model with historical software engineering data; then, the constructed recommendation model is used to predict process models for a new software project with only a few data. We also analyze the mutual impacts between process models and different types of project factors, to further help managers locate the most suitable process model. We found process models are also responsible for defect count, defect severity and software change. Experiments on the data sets from 37 different development teams of different countries show that the average recommendation accuracy of our method reaches up to 82.5%, which makes it potentially useful in practice. (C) 2016 Elsevier Inc. All rights reserved.

Clustering is one of the research hotspots in the field of data mining and has extensive applications in practice. Recently, Rodriguez and Laio [1] published a clustering algorithm on Science that identifies the clustering centers in an intuitive way and clusters objects efficiently and effectively. However, the algorithm is sensitive to a preassigned parameter and suffers from the identification of the “ideal” number of clusters. To overcome these shortages, this paper proposes a new clustering algorithm that can detect the clustering centers automatically via statistical testing. Specifically, the proposed algorithm first defines a new metric to measure the density of an object that is more robust to the preassigned parameter, further generates a metric to evaluate the centrality of each object. Afterwards, it identifies the objects with extremely large centrality metrics as the clustering centers via an outward statistical testing method. Finally, it groups the remaining objects into clusters containing their nearest neighbors with higher density. Extensive experiments are conducted over different kinds of clustering data sets to evaluate the performance of the proposed algorithm and compare with the algorithm in Science. The results show the effectiveness and robustness of the proposed algorithm.

Information retrieval based automatic bug localization techniques provide developers a ranked list of suspicious buggy source entities to aid locate the ones needed to be modified and to fix the bug. However, it is unavoidable that some buggy entities are ranked low in the result list using these automatic techniques. We assume a bug localization process to address this challenge. Each time a source code entity in the ranked list is examined, the developers will have the option as to whether to continue examining the automatic bug localization result, or simply switch to using a conventional localization approach. We propose a new evaluation metric called ETC (Expected Time Cost) in the localization process, which includes the time cost of using the conventional approach. Under our assumptions, we derived simple criteria to minimize ETC. We compared the time cost of a state-of-art automatic localization method, BugLocator, with and without using our strategy in two projects. The result shows that using our proposed strategy combining both automatic localization technique together with conventional approach performs better than using only either the automatic localization technique or the conventional approach.

Context: In a large object-oriented software system, packages play the role of modules which group related classes together to provide well-identified services to the rest of the system. In this context, it is widely believed that modularization has a large influence on the quality of packages. Recently, Sarkar, Kak, and Rama proposed a set of new metrics to characterize the modularization quality of packages from important perspectives such as inter-module call traffic, state access violations, fragile base-class design, programming to interface, and plugin pollution. These package-modularization metrics are quite different from traditional package-level metrics, which measure software quality mainly from size, extensibility, responsibility, independence, abstractness, and instability perspectives. As such, it is expected that these package-modularization metrics should be useful predictors for fault-proneness. However, little is currently known on their actual usefulness for fault-proneness prediction, especially compared with traditional package-level metrics.Objective: In this paper, we examine the role of these new package-modularization metrics for determining software fault-proneness in object-oriented systems.Method: We first use principal component analysis to analyze whether these new package-modularization metrics capture additional information compared with traditional package-level metrics. Second, we employ univariate prediction models to investigate how these new package-modularization metrics are related to fault-proneness. Finally, we build multivariate prediction models to examine the ability of these new package-modularization metrics for predicting fault-prone packages.Results: Our results, based on six open-source object-oriented software systems, show that: (I) these new package-modularization metrics provide new and complementary views of software complexity compared with traditional package-level metrics; (2) most of these new package-modularization metrics have a significant association with fault-proneness in an expected direction; and (3) these new package-modularization metrics can substantially improve the effectiveness of fault-proneness prediction when used with traditional package-level metrics together.Conclusions: The package-modularization metrics proposed by Sarkar, Kak, and Rama are useful for practitioners to develop quality software systems. (C) 2014 Elsevier B.V. All rights reserved.

Class imbalances have been reported to compromise the performance of most standard classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this problem, various solutions have been explored mainly via balancing the skewed class distribution or improving the existing classification algorithms. However, these methods pay more attention on the imbalance distribution, ignoring the discriminative ability of features in the context of class imbalance data. In this perspective, a dissimilarity-based method is proposed to deal with the classification of imbalanced data. Our proposed method first removes the useless and redundant features by feature selection from the given data set; and then, extracts representative instances from the reduced data as prototypes; finally, projects the reduced data into a dissimilarity space by constructing new features, and builds the classification model with data in the dissimilarity space. Extensive experiments over 24 benchmark class imbalance data sets show that, compared with seven other imbalance data tackling solutions, our proposed method greatly improves the performance of imbalance learning, and outperforms the other solutions with all given classification algorithms.

The problem of trip planning with time constraints aims to find the optimal routes satisfying the maximum time requirement and possessing the highest attraction score. In this paper, a more efficient algorithm TripRec is proposed to solve this problem. Based on the principle of the Aprior algorithm for mining frequent item sets, our method constructs candidate attraction sets containing k attractions by using the join rule on valid sets consisting of k-1 attractions. After all the valid routes from the valid k-1 attraction sets have been obtained, all of the candidate routes for the candidate k-sets can be acquired through a route extension approach. This method exhibits manifest improvement of the efficiency in the valid routes generation process. Then, by determining whether there exists at least one valid route, the paper prunes some candidate attraction sets to gain all the valid sets. The process will continue until no more valid attraction sets can be obtained. In addition, several optimization strategies are employed to greatly enhance the performance of the algorithm. Experimental results on both real-world and synthetic data sets show that our algorithm has the better pruning rate and efficiency compared with the state-of-the-art method.

The problem of mobile sequential recommendation is to suggest a route connecting a set of pick-up points for a taxi driver so that he/she is more likely to get passengers with less travel cost. Essentially, a key challenge of this problem is its high computational complexity. In this paper, we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem consisting of two separate stages: an offline pre-processing stage and an online search stage. The offline stage pre-computes potential candidate sequences from a set of pick-up points. A backward incremental sequence generation algorithm is proposed based on the identified iterative property of the cost function. Simultaneously, an incremental pruning policy is adopted in the process of sequence generation to reduce the search space of the potential sequences effectively. In addition, a batch pruning algorithm is further applied to the generated potential sequences to remove some non-optimal sequences of a given length. Since the pruning effectiveness keeps growing with the increase of the sequence length, at the online stage, our method can efficiently find the optimal driving route for an unloaded taxi in the remaining candidate sequences. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pick-up points in real-world applications.

Community detection is an important methodology for understanding the intrinsic structure and function of a real-world network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm (Abbreviated as DLPA), to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.

The class imbalance problems have been reported to severely hinder classification performance of many standard learning algorithms, and have attracted a great deal of attention from researchers of different fields. Therefore, a number of methods, such as sampling methods, cost-sensitive learning methods, and bagging and boosting based ensemble methods, have been proposed to solve these problems. However, these conventional class imbalance handling methods might suffer from the loss of potentially useful information, unexpected mistakes or increasing the likelihood of overfitting because they may alter the original data distribution. Thus we propose a novel ensemble method, which firstly converts an imbalanced data set into multiple balanced ones and then builds a number of classifiers on these multiple data with a specific classification algorithm. Finally, the classification results of these classifiers for new data are combined by a specific ensemble rule. In the empirical study, different class imbalance data handling methods including three conventional sampling methods, one cost-sensitive learning method, six Bagging and Boosting based ensemble methods, our previous method EM1vs1 and two fuzzy-rule based classification methods were compared with our method. The experimental results on 46 imbalanced data sets show that our proposed method is usually superior to the conventional imbalance data handling methods when solving the highly imbalanced problems.

Community detection is an important work for discovering the structure and features of complex networks. Many existing methods are sensitive to critical user-dependent parameters or time-consuming in practice. In this paper, we propose a novel label propagation algorithm, called CenLP (Centrality-based Label Propagation). The algorithm introduces a new function to measure the centrality of nodes quantitatively without any user interaction by calculating the local density and the similarity with higher density neighbors for each node. Based on the centrality of nodes, we present a new label propagation algorithm with specific update order and node preference to uncover communities in large-scale networks automatically without imposing any prior restriction. Experiments on both real-world and synthetic networks manifest our algorithm retains the simplicity, effectiveness, and scalability of the original label propagation algorithm and becomes more robust and accurate. Extensive experiments demonstrate the superior performance of our algorithm over the baseline methods. Moreover, our detailed experimental evaluation on real-world networks indicates that our algorithm can effectively measure the centrality of nodes in social networks.