Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition

In this paper, we propose a two-stage multiobjective-simulated annealing (MOSA)-based technique for named entity recognition (NER). At first, MOSA is used for feature selection under two statistical classifiers, viz. conditional random field (CRF) and support vector machine (SVM). Each solution on the final Pareto optimal front provides a different classifier. These classifiers are then combined together by using a new classifier ensemble technique based on MOSA. Several different versions of the objective functions are exploited. We hypothesize that the reliability of prediction of each classifier differs among the various output classes. Thus, in an ensemble system, it is necessary to find out the appropriate weight of vote for each output class in each classifier. We propose a MOSA-based technique to determine the weights for votes automatically. The proposed two-stage technique is evaluated for NER in Bengali, a resource-poor language, as well as for English. Evaluation results yield the highest recall, precision and F-measure values of 93.95, 95.15 and 94.55 %, respectively for Bengali and 89.01, 89.35 and 89.18 %, respectively for English. Experiments also suggest that the classifier ensemble identified by the proposed MOO-based approach optimizing the F-measure values of named entity (NE) boundary detection outperforms all the individual classifiers and four conventional baseline models.     

Forest optimization algorithm-based feature selection using classifier ensemble

Features selection is the process of choosing the relevant subset of features from the high-dimensional dataset to enhance the performance of the classifier. Much research has been carried out in the present world for the process of feature selection. Algorithms such as Naïve Bayes (NB), decision tree, and genetic algorithm are applied to the high-dimensional dataset to select the relevant features and also to increase the computational speed. The proposed model presents a solution for selection of features using ensemble classifier algorithms. The proposed algorithm is the combination of minimum redundancy and maximum relevance (mRMR) and forest optimization algorithm (FOA). Ensemble-based algorithms such as support vector machine (SVM), K-nearest neighbor (KNN), and NB is further used to enhance the performance of the classifier algorithm. The mRMR-FOA is used to select the relevant features from the various datasets and 21% to 24% improvement is recorded in the feature selection. The ensemble classifier algorithms further improves the performance of the algorithm and provides accuracy of 96%.     

Prototype selection for dynamic classifier and ensemble selection

In dynamic ensemble selection (DES) techniques, only the most competent classifiers, for the classification of a specific test sample, are selected to predict the sample’s class labels. The key in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers’ competence is usually estimated according to a given criterion, which is computed over the neighborhood of the test sample defined on the validation data, called the region of competence. A problem arises when there is a high degree of noise in the validation data, causing the samples belonging to the region of competence to not represent the query sample. In such cases, the dynamic selection technique might select the base classifier that overfitted the local region rather than the one with the best generalization performance. In this paper, we propose two modifications in order to improve the generalization performance of any DES technique. First, a prototype selection technique is applied over the validation data to reduce the amount of overlap between the classes, producing smoother decision borders. During generalization, a local adaptive K-Nearest Neighbor algorithm is used to minimize the influence of noisy samples in the region of competence. Thus, DES techniques can better estimate the classifiers’ competence. Experiments are conducted using 10 state-of-the-art DES techniques over 30 classification problems. The results demonstrate that the proposed scheme significantly improves the classification accuracy of dynamic selection techniques.

     

Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition

In this paper, the concept of finding an appropriate classifier ensemble for named entity recognition is posed as a multiobjective optimization (MOO) problem. Our underlying assumption is that instead of searching for the best-fitting feature set for a particular classifier, ensembling of several classifiers those are trained using different feature representations could be a more fruitful approach, but it is crucial to determine the appropriate subset of classifiers that are most suitable for the ensemble. We use three heterogenous classifiers namely maximum entropy, conditional random field, and support vector machine in order to build a number of models depending upon the various representations of the available features. The proposed MOO-based ensemble technique is evaluated for three resource-constrained languages, namely Bengali, Hindi, and Telugu. Evaluation results yield the recall, precision, and F-measure values of 92.21, 92.72, and 92.46%, respectively, for Bengali; 97.07, 89.63, and 93.20%, respectively, for Hindi; and 80.79, 93.18, and 86.54%, respectively, for Telugu. We also evaluate our proposed technique with the CoNLL-2003 shared task English data sets that yield the recall, precision, and F-measure values of 89.72, 89.84, and 89.78%, respectively. Experimental results show that the classifier ensemble identified by our proposed MOO-based approach outperforms all the individual classifiers, two different conventional baseline ensembles, and the classifier ensemble identified by a single objective?Cbased approach. In a part of the paper, we formulate the problem of feature selection in any classifier under the MOO framework and show that our proposed classifier ensemble attains superior performance to it.     

Integration of classifier diversity measures for feature selection-based classifier ensemble reduction

A classifier ensemble combines a set of individual classifier’s predictions to produce more accurate results than that of any single classifier system. However, one classifier ensemble with too many classifiers may consume a large amount of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a novel classifier ensemble reduction framework. The framework converts the ensemble reduction into an optimization problem and uses the harmony search algorithm to find the optimized classifier ensemble. Both pairwise and non-pairwise diversity measure algorithms are applied by the subset evaluation method. For the pairwise diversity measure, three conventional diversity algorithms and one new diversity measure method are used to calculate the diversity’s merits. For the non-pairwise diversity measure, three classical algorithms are used. The proposed subset evaluation methods are demonstrated by the experimental data. In comparison with other classifier ensemble methods, the method implemented by the measurement of the interrater agreement exhibits a high accuracy prediction rate against the current ensembles’ performance. In addition, the framework with the new diversity measure achieves relatively good performance with less computational time.     

Diversity-aware classifier ensemble selection via f-score

The primary effect of using a reduced number of classifiers is a reduction in the computational requirements during learning and classification time. In addition to this obvious result, research shows that the fusion of all available classifiers is not a guarantee of best performance but good results on the average. The much researched issue of whether it is more convenient to fuse or to select has become even more of interest in recent years with the development of the Online Boosting theory, where a limited set of classifiers is continuously updated as new inputs are observed and classifications performed. The concept of online classification has recently received significant interest in the computer vision community. Classifiers can be trained on the visual features of a target, casting the tracking problem into a binary classification one: distinguishing the target from the background.Here we discuss how to optimize the performance of a classifier ensemble employed for target tracking in video sequences. In particular, we propose the F-score measure as a novel means to select the members of the ensemble in a dynamic fashion. For each frame, the ensemble is built as a subset of a larger pool of classifiers selecting its members according to their F-score. We observed an overall increase in classification accuracy and a general tendency in redundancy reduction among the members of an f-score optimized ensemble. We carried out our experiments both on benchmark binary datasets and standard video sequences.     

From dynamic classifier selection to dynamic ensemble selection

In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method.     

多目标混沌差分进化算法

将差分进化算法用于多目标优化问题,提出了多目标混沌差分进化算法(CDEMO).该算法利用混沌序列初始化种群,并用混沌备用种群进行替换操作.该操作不仅起到了维持非劣最优解集均匀性的作用,而且增强了算法的搜索功能.对CDEMO的性能进行研究,数值实验结果表明了CDEMO的有效性.     

Self-adaptive differential evolution for feature selection in hyperspectral image data

Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Each pixel of an image is represented by a vector where the components of the vector constitute the reflectance value of the surface for each of the bands. The length of the vector is equal to the number of bands. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. Moreover, higher correlation among neighboring bands increases the redundancy among them. As a result, feature selection becomes very essential for reducing the dimensionality. In the proposed work, an attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Self-adaptive differential evolution (SADE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on three sets of data and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.     

基于分步特征提取和组合分类器的电信客户流失预测模型

针对电信客户流失数据集存在的数据维度过高及单一分类器预测效果较弱的问题,结合过滤式和封装式特征选择方法的优点及组合分类器的较高预测能力,提出了一种基于Fisher比率与预测风险准则的分步特征选择方法结合组合分类器的电信客户流失预测模型。首先,基于Fisher比率从原始特征集合中提取具有较高判别能力的特征;其次,采用预测风险准则进一步选取对分类模型预测效果影响较大的特征;最后,构建基于平均概率输出和加权概率输出的组合分类器,以进一步提高客户流失预测效果。实验结果表明,相对于单步特征提取和单分类器模型,该方法能够提高对客户流失预测的效果。     

A dynamic classifier ensemble selection approach for noise data

Dynamic classifier ensemble selection (DCES) plays a strategic role in the field of multiple classifier systems. The real data to be classified often include a large amount of noise, so it is important to study the noise-immunity ability of various DCES strategies. This paper introduces a group method of data handling (GMDH) to DCES, and proposes a novel dynamic classifier ensemble selection strategy GDES-AD. It considers both accuracy and diversity in the process of ensemble selection. We experimentally test GDES-AD and six other ensemble strategies over 30 UCI data sets in three cases: the data sets do not include artificial noise, include class noise, and include attribute noise. Statistical analysis results show that GDES-AD has stronger noise-immunity ability than other strategies. In addition, we find out that Random Subspace is more suitable for GDES-AD compared with Bagging. Further, the bias-variance decomposition experiments for the classification errors of various strategies show that the stronger noise-immunity ability of GDES-AD is mainly due to the fact that it can reduce the bias in classification error better.     

基于差分进化的多目标粒子群特征选择算法

特征选择技术在大数据分析、图像处理、生物信息学等领域具有重要作用。在实际应用中,降低分类错误率和减少提取出的特征数量便于后续数据的利用,往往是两个冲突的目标。基于拥挤、变异和支配策略的多目标粒子群特征选择（crowding,mutation,dominance particle swarm optimization for feature selection,CMDPSOFS）算法是一种面向特征选择应用中特征数量最小和分类错误率最低的双目标优化算法。它使用三种不同的变异机制,用于保持群体多样性和平衡全局、局部搜索的能力,但其中的均匀变异使算法的随机性大大增加,产生较多适应值差的解,降低了算法收敛速度。改进的CMDPSOFS-II算法将差分进化算法中的变异算子和选择操作引入到CMDPSOFS算法中,实验结果表明,CMDPSOFS-II算法在特征选择上得到比原来的方法更优的结果,更好地平衡了全局和局部搜索能力。     

Nearest neighbor classifier: Simultaneous editing and feature selection

Nearest neighbor classifiers demand significant computational resources (time and memory). Editing of the reference set and feature selection are two different approaches to this problem. Here we encode the two approaches within the same genetic algorithm (GA) and simultaneously select features and reference cases. Two data sets were used: the SATIMAGE data and a generated data set. The GA was found to be an expedient solution compared to editing followed by feature selection, feature selection followed by editing, and the individual results from feature selection and editing.     

Integrated classifier hyperplane placement and feature selection

The process of placing a separating hyperplane for data classification is normally disconnected from the process of selecting the features to use. An approach for feature selection that is conceptually simple but computationally explosive is to simply apply the hyperplane placement process to all possible subsets of features, selecting the smallest set of features that provides reasonable classification accuracy. Two ways to speed this process are (i) use a faster filtering criterion instead of a complete hyperplane placement, and (ii) use a greedy forward or backwards sequential selection method. This paper introduces a new filtering criterion that is very fast: maximizing the drop in the sum of infeasibilities in a linear-programming transformation of the problem. It also shows how the linear programming transformation can be applied to reduce the number of features after a separating hyperplane has already been placed while maintaining the separation that was originally induced by the hyperplane. Finally, a new and highly effective integrated method that simultaneously selects features while placing the separating hyperplane is introduced.     

A probabilistic model of classifier competence for dynamic ensemble selection

The concept of a classifier competence is fundamental to multiple classifier systems (MCSs). In this study, a method for calculating the classifier competence is developed using a probabilistic model. In the method, first a randomised reference classifier (RRC) whose class supports are realisations of the random variables with beta probability distributions is constructed. The parameters of the distributions are chosen in such a way that, for each feature vector in a validation set, the expected values of the class supports produced by the RRC and the class supports produced by a modelled classifier are equal. This allows for using the probability of correct classification of the RRC as the competence of the modelled classifier. The competences calculated for a validation set are then generalised to an entire feature space by constructing a competence function based on a potential function model or regression. Three systems based on a dynamic classifier selection and a dynamic ensemble selection (DES) were constructed using the method developed. The DES based system had statistically significant higher average rank than the ones of eight benchmark MCSs for 22 data sets and a heterogeneous ensemble. The results obtained indicate that the full vector of class supports should be used for evaluating the classifier competence as this potentially improves performance of MCSs.     

Diversity in search strategies for ensemble feature selection

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subsets. A number of ways are known to quantify diversity in ensembles of classifiers, and little research has been done about their appropriateness to ensemble feature selection. In this paper, we compare five measures of diversity with regard to their possible use in ensemble feature selection. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the ensemble accuracy and other characteristics for the ensembles built with ensemble feature selection based on the considered measures of diversity. We consider four search strategies for ensemble feature selection together with the simple random subspacing: genetic search, hill-climbing, and ensemble forward and backward sequential selection. In the experiments, we show that, in some cases, the ensemble feature selection process can be sensitive to the choice of the diversity measure, and that the question of the superiority of a particular measure depends on the context of the use of diversity and on the data being processed. In many cases and on average, the plain disagreement measure is the best. Genetic search, kappa, and dynamic voting with selection form the best combination of a search strategy, diversity measure and integration method.     

Unsupervised feature selection with ensemble learning

Machine Learning - In this paper, we show that the way internal estimates are used to measure variable importance in Random Forests are also applicable to feature selection in unsupervised...     

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.     

Salient feature and reliable classifier selection for facial expression classification

A novel facial expression classification (FEC) method is presented and evaluated. The classification process is decomposed into multiple two-class classification problems, a choice that is analytically justified, and unique sets of features are extracted for each classification problem. Specifically, for each two-class problem, an iterative feature selection process that utilizes a class separability measure is employed to create salient feature vectors (SFVs), where each SFV is composed of a selected feature subset. Subsequently, two-class discriminant analysis is applied on the SFVs to produce salient discriminant hyper-planes (SDHs), which are used to train the corresponding two-class classifiers. To properly integrate the two-class classification results and produce the FEC decision, a computationally efficient and fast classification scheme is developed. During each step of this scheme, the most reliable classifier is identified and utilized, thus, a more accurate final classification decision is produced. The JAFFE and the MMI databases are used to evaluate the performance of the proposed salient-feature-and-reliable-classifier selection (SFRCS) methodology. Classification rates of 96.71% and 93.61% are achieved under the leave-one-sample-out evaluation strategy, and 85.92% under the leave-one-subject-out evaluation strategy.     

Genetic programming for simultaneous feature selection and classifier design.

This paper presents an online feature selection algorithm using genetic programming (GP). The proposed GP methodology simultaneously selects a good subset of features and constructs a classifier using the selected features. For a c-class problem, it provides a classifier having c trees. In this context, we introduce two new crossover operations to suit the feature selection process. As a byproduct, our algorithm produces a feature ranking scheme. We tested our method on several data sets having dimensions varying from 4 to 7129. We compared the performance of our method with results available in the literature and found that the proposed method produces consistently good results. To demonstrate the robustness of the scheme, we studied its effectiveness on data sets with known (synthetically added) redundant/bad features.