A novel hybrid approach for feature selection in software product lines

Software Product Line (SPL) customizes software by combining various existing features of the software with multiple variants. The main challenge is selecting valid features considering the constraints of the feature model. To solve this challenge, a hybrid approach is proposed to optimize the feature selection problem in software product lines. The Hybrid approach ‘Hyper-PSOBBO’ is a combination of Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO) and hyper-heuristic algorithms. The proposed algorithm has been compared with Bird Swarm Algorithm (BSA), PSO, BBO, Firefly, Genetic Algorithm (GA) and Hyper-heuristic. All these algorithms are performed in a set of 10 feature models that vary from a small set of 100 to a high-quality data set of 5000. The detailed empirical analysis in terms of performance has been carried out on these feature models. The results of the study indicate that the performance of the proposed method is higher to other state-of-the-art algorithms.

     

A new hybrid feature selection approach using feature association map for supervised and unsupervised classification

Feature selection, both for supervised as well as for unsupervised classification is a relevant problem pursued by researchers for decades. There are multiple benchmark algorithms based on filter, wrapper and hybrid methods. These algorithms adopt different techniques which vary from traditional search-based techniques to more advanced nature inspired algorithm based techniques. In this paper, a hybrid feature selection algorithm using graph-based technique has been proposed. The proposed algorithm has used the concept of Feature Association Map (FAM) as an underlying foundation. It has used graph-theoretic principles of minimal vertex cover and maximal independent set to derive feature subset. This algorithm applies to both supervised and unsupervised classification. The performance of the proposed algorithm has been compared with several benchmark supervised and unsupervised feature selection algorithms and found to be better than them. Also, the proposed algorithm is less computationally expensive and hence has taken less execution time for the publicly available datasets used in the experiments, which include high-dimensional datasets.     

A new local search based hybrid genetic algorithm for feature selection

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a reduced size. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS process. The local search technique works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim is to guide the search process so that the newly generated offsprings can be adjusted by the less correlated (distinct) features consisting of general and special characteristics of a given dataset. Thus, the proposed HGAFS receives the reduced redundancy of information among the selected features. On the other hand, HGAFS emphasizes on selecting a subset of salient features with reduced number using a subset size determination scheme. We have tested our HGAFS on 11 real-world classification datasets having dimensions varying from 8 to 7129. The performances of HGAFS have been compared with the results of other existing ten well-known FS algorithms. It is found that, HGAFS produces consistently better performances on selecting the subsets of salient features with resulting better classification accuracies.     

A novel hybrid algorithm for feature selection

Feature selection is an important filtering method for data analysis, pattern classification, data mining, and so on. Feature selection reduces the number of features by removing irrelevant and redundant data. In this paper, we propose a hybrid filter–wrapper feature subset selection algorithm called the maximum Spearman minimum covariance cuckoo search (MSMCCS). First, based on Spearman and covariance, a filter algorithm is proposed called maximum Spearman minimum covariance (MSMC). Second, three parameters are proposed in MSMC to adjust the weights of the correlation and redundancy, improve the relevance of feature subsets, and reduce the redundancy. Third, in the improved cuckoo search algorithm, a weighted combination strategy is used to select candidate feature subsets, a crossover mutation concept is used to adjust the candidate feature subsets, and finally, the filtered features are selected into optimal feature subsets. Therefore, the MSMCCS combines the efficiency of filters with the greater accuracy of wrappers. Experimental results on eight common data sets from the University of California at Irvine Machine Learning Repository showed that the MSMCCS algorithm had better classification accuracy than the seven wrapper methods, the one filter method, and the two hybrid methods. Furthermore, the proposed algorithm achieved preferable performance on the Wilcoxon signed-rank test and the sensitivity–specificity test.     

A soft computing framework for software effort estimation

Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. In this paper, we present a soft computing framework to tackle this challenging problem. We first use a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. One feature of this framework is that the architecture is inherently independent of the choice of algorithmic models or the nature of the estimation problems. By integrating neural networks, fuzzy logic and algorithmic models into one scheme, this framework has learning ability, integration capability of both expert knowledge and project data, good interpretability, and robustness to imprecise and uncertain inputs. Validation using industry project data shows that the framework produces good results when used to predict software cost.     

A hybrid approach of differential evolution and artificial bee colony for feature selection

“Dimensionality” is one of the major problems which affect the quality of learning process in most of the machine learning and data mining tasks. Having high dimensional datasets for training a classification model may lead to have “overfitting” of the learned model to the training data. Overfitting reduces generalization of the model, therefore causes poor classification accuracy for the new test instances. Another disadvantage of dimensionality of dataset is to have high CPU time requirement for learning and testing the model. Applying feature selection to the dataset before the learning process is essential to improve the performance of the classification task. In this study, a new hybrid method which combines artificial bee colony optimization technique with differential evolution algorithm is proposed for feature selection of classification tasks. The developed hybrid method is evaluated by using fifteen datasets from the UCI Repository which are commonly used in classification problems. To make a complete evaluation, the proposed hybrid feature selection method is compared with the artificial bee colony optimization, and differential evolution based feature selection methods, as well as with the three most popular feature selection techniques that are information gain, chi-square, and correlation feature selection. In addition to these, the performance of the proposed method is also compared with the studies in the literature which uses the same datasets. The experimental results of this study show that our developed hybrid method is able to select good features for classification tasks to improve run-time performance and accuracy of the classifier. The proposed hybrid method may also be applied to other search and optimization problems as its performance for feature selection is better than pure artificial bee colony optimization, and differential evolution.     

Cloud computing system risk estimation and service selection approach based on cloud focus theory

The main cloud computing service providers usually provide cross-regional and services of Crossing Multi-Internet Data Centers that supported with selection strategy of service level agreement risk constraint. But the traditional quality of service (QoS)-aware Web service selection approach cannot ensure the real-time and the reliability of services selection. We proposed a cloud computing system risk assessment method based on cloud theory, and generated the five property clouds by collecting the risk value and four risk indicators from each virtual machine. The cloud backward generator integrated these five clouds into one cloud, according to the weight matrix. So the risk prediction value is transferred to the risk level quantification. Then we tested the Web service selection experiments by using risk assessment level as QoS mainly constraint and comparing with LRU and MAIS methods. The result showed that the success rate and efficiency of risk assessment with cloud focus theory Web services selection approaches are more quickly and efficient.

     

Combined soft computing model for value stock selection based on fundamental analysis

The stock selection problem is one of the major issues in the investment industry, which is mainly solved by analyzing financial ratios. However, considering the complexity and imprecise patterns of the stock market, obvious and easy-to-understand investment rules, based on fundamental analysis, are difficult to obtain. Therefore, in this paper, we propose a combined soft computing model for tackling the value stock selection problem, which includes dominance-based rough set approach, formal concept analysis, and decision-making trial and evaluation laboratory technique. The objectives of the proposed approach are to (1) obtain easy-to-understand decision rules, (2) identify the core attributes that may distinguish value stocks, (3) explore the cause–effect relationships among the attributes or criteria in the strong decision rules to gain more insights. To examine and illustrate the proposed model, this study used a group of IT stocks in Taiwan as an empirical case. The findings contribute to the in-depth understanding of the value stock selection problem in practice.     

Partner selection model and soft computing approach for dynamic alliance of enterprises

Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0-1 integer programming with non-analytical objective function. Then, the solution space is reduced by defining the inefficient candidate. By using the fuzzy rule quantification method, a fuzzy logic based decision making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded genetic algorithm. We compare the algorithm with tranditional methods. The results show that the suggested approach can quickly achieve optimal solution for large size problems with high probability. The approach was applied to the partner selection problem of a coal fire power station construction project. The satisfactory results have been achieved.     

A novel approach to feature selection based on analysis of classregions

This paper presents a novel approach to feature selection based on analysis of class regions which are generated by a fuzzy classifier. A measure for feature evaluation is proposed and is defined as the exception ratio. The exception ratio represents the degree of overlaps in the class regions, in other words, the degree of having exceptions inside of fuzzy rules generated by the fuzzy classifier. It is shown that for a given set of features, a subset of features that has the lowest sum of the exception ratios has the tendency to contain the most relevant features, compared to the other subsets with the same number of features. An algorithm is then proposed that performs elimination of irrelevant features. Given a set of remaining features, the algorithm eliminates the next feature, the elimination of which minimizes the sum of the exception ratios. Next, a terminating criterion is given. Based on this criterion, the proposed algorithm terminates when a significant increase in the sum of the exception ratios occurs due to the next elimination. Experiments show that the proposed algorithm performs well in eliminating irrelevant features while constraining the increase in recognition error rates for unknown data of the classifiers in use.     

A hybrid filter/wrapper approach of feature selection using information theory

We focus on a hybrid approach of feature selection. We begin our analysis with a filter model, exploiting the geometrical information contained in the minimum spanning tree (MST) built on the learning set. This model exploits a statistical test of relative certainty gain, used in a forward selection algorithm. In the second part of the paper, we show that the MST can be replaced by the 1 nearest-neighbor graph without challenging the statistical framework. This leads to a feature selection algorithm belonging to a new category of hybrid models (filter-wrapper). Experimental results on readily available synthetic and natural domains are presented and discussed.     

基于谱分析的无监督特征选择算法

为提高无监督状态下特征选择的准确度,基于图谱理论证明了标准化Laplacian矩阵前K个最小特征值分布表现了簇结构的可识别性,使用谱分析方法指导无监督特征选择,提出了特征重要度排序算法。对大容量数据集特征选择,应用Nystrm方法降低时间复杂度。实验结果表明,使用该算法与使用主流无监督特征选择方法及全部特征相比能得到更高的聚类性能评分。     

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness.     

A feature selection approach based on a similarity measure for software defect prediction

Software defect prediction is aimed to find potential defects based on historical data and software features. Software features can reflect the characteristics of software modules. However, some of these features may be more relevant to the class (defective or non-defective), but others may be redundant or irrelevant. To fully measure the correlation between different features and the class, we present a feature selection approach based on a similarity measure (SM) for software defect prediction. First, the feature weights are updated according to the similarity of samples in different classes. Second, a feature ranking list is generated by sorting the feature weights in descending order, and all feature subsets are selected from the feature ranking list in sequence. Finally, all feature subsets are evaluated on a k-nearest neighbor (KNN) model and measured by an area under curve (AUC) metric for classification performance. The experiments are conducted on 11 National Aeronautics and Space Administration (NASA) datasets, and the results show that our approach performs better than or is comparable to the compared feature selection approaches in terms of classification performance.     

A new approach to feature selection based on the Karhunen-Loeve expansion

After surveying existing feature selection procedures based upon the Karhunen-Loeve (K-L) expansion, the paper describes a new K-L technique that overcomes some of the limitations of the earlier procedures. The new method takes into account information on both the class variances and means, but lays particular emphasis on the classification potential of the latter. The results of a series of experiments concerned with the classification of real vector-electrocardiogram and artificially generated data demonstrate the advantages of the new method. They suggest that it is particularly useful for pattern recognition when combined with classification procedures based upon discriminant functions obtained by recursive least squares analysis.     

一种基于信息增益的特征优化选择方法

特征选择是文本分类的一个重要环节,它可以有效提高分类精度和效率。在研究文本分类特征选择方法的基础上,分析了信息增益方法的不足,将频度、集中度、分散度应用到信息增益方法上,提出了一种基于信息增益的特征优化选择方法。实验表明,该方法在分类效果与性能上都优于传统方法。     

Occupancy estimation from environmental parameters using wrapper and hybrid feature selection

Occupancy information is essential to facilitate demand-driven operations of air-conditioning and mechanical ventilation (ACMV) systems. Environmental sensors are increasingly being explored as cost effective and non-intrusive means to obtain the occupancy information. This requires the extraction and selection of useful features from the sensor data. In past works, feature selection has generally been implemented using filter-based approaches. In this work, we introduce the use of wrapper and hybrid feature selection for better occupancy estimation. To achieve a fast computation time, we introduce a ranking-based incremental search in our algorithms, which is more efficient than the exhaustive search used in past works. For wrapper feature selection, we propose the WRANK-ELM, which searches an ordered list of features using the extreme learning machine (ELM) classifier. For hybrid feature selection, we propose the RIG-ELM, which is a filter–wrapper hybrid that uses the relative information gain (RIG) criterion for feature ranking and the ELM for the incremental search. We present experimental results in an office space with a multi-sensory network to validate the proposed algorithms.     

基于随机森林深度特征选择的人体姿态估计

针对以随机森林为分类器的人体姿态估计系统内存占用过大的问题,提出一种优化的随机森林模型,该模型在进行Bootstrap抽样前,引入Poisson过程并将其与深度信息相融合组建一个滤过网对原始训练数据集进行过滤,将一部分对后续分类起到非积极作用的特征样本点滤除,使训练数据集得到优化重构,进而较好地弥补随机森林在抽样过程中重复抽样以及重抽样样本代表性不强的缺点。实验结果表明了该优化模型的有效性,大大降低了系统的时间、空间复杂度,使得系统的适用性更强。     

Soft computing models based feature selection for TRUS prostate cancer image classification

Ultrasound imaging is the most suitable method for early detection of prostate cancer. It is very difficult to distinguish benign and malignant nature of the affliction in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based classification system can provide a second opinion to the radiologists. Generally, objects are described in terms of a set of measurable features in pattern recognition. The selection and quality of the features representing each pattern will have a considerable bearing on the success of subsequent pattern classification. Feature selection is a process of selecting the most wanted or dominating features set from the original features set in order to reduce the cost of data visualization and increasing classification efficiency and accuracy. The region of interest (ROI) is identified from transrectal ultrasound (TRUS) images using DBSCAN clustering with morphological operators after image enhancement using M3-filter. Then the 22 grey level co-occurrence matrix features are extracted from the ROIs. Soft computing model based feature selection algorithms genetic algorithm (GA), ant colony optimization (ACO) and QR are studied. In this paper, QR-ACO (hybridization of rough set based QR and ACO) and GA-ACO (hybridization GA and ACO) are proposed for reducing feature set in order to increase the accuracy and efficiency of the classification with regard to prostate cancer. The selected features may have the best discriminatory power for classifying prostate cancer based on TRUS images. Support vector machine is tailored for evaluation of the proposed feature selection methods through classification. Then, the comparative analysis is performed among these methods. Experimental results show that the proposed method QR-ACO produces significant results. Number of features selected using QR-ACO algorithm is minimal, is successful and has high detection accuracy.