一种基于特征增强和决策融合的人体行为识别方法

利用三轴加速度传感器进行人体行为识别一直是传感器数据处理、模式识别领域的研究热点。加速度数据往往存在着多种动作数据难以区分的情况,特别是走、上楼、下楼这3个动作数据非常相似,这给正确识别这3种人体动作带来了较大的难度。提出一种基于特征增强与决策融合的行为识别方法,通过对部分特征值进行增强处理和对多个分类结果进行决策融合来识别走、上楼、下楼这些难以区分的相似动作。实验验证,所提方法可克服由于加速度数据的相似性而导致的动作识别正确率低、识别误差大的情况,有效提高人体行为识别率,且可在实际应用中实时识别人体行为动作。     

Locally linear reconstruction for instance-based learning

Instance-based learning (IBL), so called memory-based reasoning (MBR), is a commonly used non-parametric learning algorithm. k-nearest neighbor (k-NN) learning is the most popular realization of IBL. Due to its usability and adaptability, k-NN has been successfully applied to a wide range of applications. However, in practice, one has to set important model parameters only empirically: the number of neighbors (k) and weights to those neighbors. In this paper, we propose structured ways to set these parameters, based on locally linear reconstruction (LLR). We then employed sequential minimal optimization (SMO) for solving quadratic programming step involved in LLR for classification to reduce the computational complexity. Experimental results from 11 classification and eight regression tasks were promising enough to merit further investigation: not only did LLR outperform the conventional weight allocation methods without much additional computational cost, but also LLR was found to be robust to the change of k.     

k-NN classification of handwritten characters via accelerated GAT correlation

This paper addresses the problem of reinforcing the ability of the k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although the k-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching in k-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.     

Distributed adaptive top-k monitoring in wireless sensor networks

Top-k monitoring queries are useful in many wireless sensor network applications. A query of this type continuously returns a list of k ordered nodes with the highest (or lowest) sensor readings. To process these queries, a well-known approach is to install a filter at each sensor node to avoid unnecessary transmissions of sensor readings. In this paper, we propose a new top-k monitoring method, named Distributed Adaptive Filter-based Monitoring. In this method, we first propose a new query reevaluation algorithm that works distributedly in the network to reduce the communication cost of sending probe messages. Then, we present an adaptive filter updating algorithm which is based on predicted benefits to lower down the transmission cost of sending updated filters to the sensor nodes. Experimental results on real data traces show that our proposed method performs much better than the other existing methods in terms of both network lifetime and average energy consumption.     

FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors

We propose an efficient approach, FSKNN, which employs fuzzy similarity measure (FSM) and k nearest neighbors (KNN), for multi-label text classification. One of the problems associated with KNN-like approaches is its demanding computational cost in finding the k nearest neighbors from all the training patterns. For FSKNN, FSM is used to group the training patterns into clusters. Then only the training documents in those clusters whose fuzzy similarities to the document exceed a predesignated threshold are considered in finding the k nearest neighbors for the document. An unseen document is labeled based on its k nearest neighbors using the maximum a posteriori estimate. Experimental results show that our proposed method can work more effectively than other methods.     

Supervised parametric and non-parametric classification of chromosome images

This paper describes a fully automatic chromosome classification algorithm for Multiplex Fluorescence In Situ Hybridization (M-FISH) images using supervised parametric and non-parametric techniques. M-FISH is a recently developed chromosome imaging method in which each chromosome is labelled with 5 fluors (dyes) and a DNA stain. The classification problem is modelled as a 25-class 6-feature pixel-by-pixel classification task. The 25 classes are the 24 types of human chromosomes and the background, while the six features correspond to the brightness of the dyes at each pixel. Maximum likelihood estimation, nearest neighbor and k-nearest neighbor methods are implemented for the classification. The highest classification accuracy is achieved with the k-nearest neighbor method and k=7 is an optimal value for this classification task.     

Improved k-nearest neighbor classification

k-nearest neighbor (k-NN) classification is a well-known decision rule that is widely used in pattern classification. However, the traditional implementation of this method is computationally expensive. In this paper we develop two effective techniques, namely, template condensing and preprocessing, to significantly speed up k-NN classification while maintaining the level of accuracy. Our template condensing technique aims at “sparsifying” dense homogeneous clusters of prototypes of any single class. This is implemented by iteratively eliminating patterns which exhibit high attractive capacities. Our preprocessing technique filters a large portion of prototypes which are unlikely to match against the unknown pattern. This again accelerates the classification procedure considerably, especially in cases where the dimensionality of the feature space is high. One of our case studies shows that the incorporation of these two techniques to k-NN rule achieves a seven-fold speed-up without sacrificing accuracy.     

Privacy-preserving data mining: A feature set partitioning approach

In privacy-preserving data mining (PPDM), a widely used method for achieving data mining goals while preserving privacy is based on k-anonymity. This method, which protects subject-specific sensitive data by anonymizing it before it is released for data mining, demands that every tuple in the released table should be indistinguishable from no fewer than k subjects. The most common approach for achieving compliance with k-anonymity is to replace certain values with less specific but semantically consistent values. In this paper we propose a different approach for achieving k-anonymity by partitioning the original dataset into several projections such that each one of them adheres to k-anonymity. Moreover, any attempt to rejoin the projections, results in a table that still complies with k-anonymity. A classifier is trained on each projection and subsequently, an unlabelled instance is classified by combining the classifications of all classifiers.Guided by classification accuracy and k-anonymity constraints, the proposed data mining privacy by decomposition (DMPD) algorithm uses a genetic algorithm to search for optimal feature set partitioning. Ten separate datasets were evaluated with DMPD in order to compare its classification performance with other k-anonymity-based methods. The results suggest that DMPD performs better than existing k-anonymity-based algorithms and there is no necessity for applying domain dependent knowledge. Using multiobjective optimization methods, we also examine the tradeoff between the two conflicting objectives in PPDM: privacy and predictive performance.     

Improved differential evolution algorithms for solving generalized assignment problem

This paper presents algorithms based on differential evolution (DE) to solve the generalized assignment problem (GAP) with the objective to minimize the assignment cost under the limitation of the agent capacity. Three local search techniques: shifting, exchange, and k-variable move algorithms are added to the DE algorithm in order to improve the solutions. Eight DE-based algorithms are presented, each of which uses DE with a different combination of local search techniques. The experiments are carried out using published standard instances from the literature. The best proposed algorithm using shifting and k-variable move as the local search (DE-SK) techniques was used to compare its performance with those of Bee algorithm (BEE) and Tabu search algorithm (TABU). The computational results revealed that the BEE and DE-SK are not significantly different while the DE-SK outperforms the TABU algorithm. However, even though the statistical test shows that DE-SK is not significantly different compared with the BEE algorithm, the DE-SK is able to obtain more optimal solutions (87.5%) compared to the BEE algorithm that can obtain only 12.5% optimal solutions. This is because the DE-SK is designed to enhance the search capability by improving the diversification using the DE's operators and the k-variable moves added to the DE can improve the intensification. Hence, the proposed algorithms, especially the DE-SK, can be used to solve various practical cases of GAP and other combinatorial optimization problems by enhancing the solution quality, while still maintaining fast computational time.     

Liver fibrosis staging using CT image texture analysis and soft computing

Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws’ method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws’ texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons.     

SVOIS: Support Vector Oriented Instance Selection for text classification

Automatic text classification is usually based on models constructed through learning from training examples. However, as the size of text document repositories grows rapidly, the storage requirements and computational cost of model learning is becoming ever higher. Instance selection is one solution to overcoming this limitation. The aim is to reduce the amount of data by filtering out noisy data from a given training dataset. A number of instance selection algorithms have been proposed in the literature, such as ENN, IB3, ICF, and DROP3. However, all of these methods have been developed for the k-nearest neighbor (k-NN) classifier. In addition, their performance has not been examined over the text classification domain where the dimensionality of the dataset is usually very high. The support vector machines (SVM) are core text classification techniques. In this study, a novel instance selection method, called Support Vector Oriented Instance Selection (SVOIS), is proposed. First of all, a regression plane in the original feature space is identified by utilizing a threshold distance between the given training instances and their class centers. Then, another threshold distance, between the identified data (forming the regression plane) and the regression plane, is used to decide on the support vectors for the selected instances. The experimental results based on the TechTC-100 dataset show the superior performance of SVOIS over other state-of-the-art algorithms. In particular, using SVOIS to select text documents allows the k-NN and SVM classifiers perform better than without instance selection.     

Quadratic-radial-basis-function-kernel for classifying multi-class agricultural datasets with continuous attributes

Classification of agricultural data such as soil data and crop data is significant as it allows the stakeholders to make meaningful decisions for farming. Soil classification aids farmers in deciding the type of crop to be sown for a particular type of soil. Similarly, wheat variety classification assists in selecting the right type of wheat for a particular product. Current methods used for classifying agricultural data are mostly manual. These methods involve agriculture field visits and surveys and are labor-intensive, expensive, and prone to human error. Recently, data mining techniques such as decision trees, k-nearest neighbors (k-NN), support vector machine (SVM), and Naive Bayes (NB) have been used in classification of agricultural data such as soil, crops, and land cover. The resulting classification aid the decision making process of government organizations and agro-industries in the field of agriculture. SVM is a popular approach for data classification. A recent study on SVM highlighted the fact that using multiple kernels instead of a single kernel would lead to better performance because of the greater learning and generalization power. In this work, a hybrid kernel based support vector machine (H-SVM) is proposed for classifying multi-class agricultural datasets having continuous attributes. Genetic algorithm (GA) or gradient descent (GD) methods are utilized to select the SVM parameters C and γ. The proposed kernel is called the quadratic-radial-basis-function kernel (QRK) and it combines both quadratic and radial basis function (RBF) kernels. The proposed classifier has the ability to classify all kinds of multi-class agricultural datasets with continuous features. Rigorous experiments using the proposed method are performed on standard benchmark and real world agriculture datasets. The results reveal a significant performance improvement over state of the art methods such as NB, k-NN, and SVM in terms of performance metrics such as accuracy, sensitivity, specificity, precision, and F-score.     

The global k-means clustering algorithm

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts.     

Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals

In this work, an attempt has been made to differentiate surface electromyography (sEMG) signals under muscle fatigue and non-fatigue conditions with multiple time window (MTW) features. sEMG signals are recorded from biceps brachii muscles of 50 volunteers. Eleven MTW features are extracted from the acquired signals using four window functions, namely rectangular windows, Hamming windows, trapezoidal windows, and Slepian windows. Prominent features are selected using genetic algorithm and information gain based ranking. Four different classification algorithms, namely naïve Bayes, support vector machines, k-nearest neighbour, and linear discriminant analysis, are used for the study. Classifier performances with the MTW features are compared with the currently used time- and frequency-domain features. The results show a reduction in mean and median frequencies of the signals under fatigue. Mean and variance of the features differ by an order of magnitude between the two cases considered. The number of features is reduced by 45% with the genetic algorithm and 36% with information gain based ranking. The k-nearest neighbour algorithm is found to be the most accurate in classifying the features, with a maximum accuracy of 93% with the features selected using information gain ranking.     

A hybrid expert system approach for telemonitoring of vocal fold pathology

Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. This paper proposes a hybrid expert system approach to detect vocal fold pathology using the compressed/low quality voice samples which includes feature extraction using wavelet packet transform, clustering based feature weighting and classification. In order to improve the robustness and discrimination ability of the wavelet packet transform based features (raw features), we propose clustering based feature weighting methods including k-means clustering (KMC), fuzzy c-means (FCM) clustering and subtractive clustering (SBC). We have investigated the effectiveness of raw and weighted features (obtained after applying feature weighting methods) using four different classifiers: Least Square Support Vector Machine (LS-SVM) with radial basis kernel, k-means nearest neighbor (kNN) classifier, probabilistic neural network (PNN) and classification and regression tree (CART). The proposed hybrid expert system approach gives a promising classification accuracy of 100% using the feature weighting methods and also it has potential application in remote detection of vocal fold pathology.     

Evaluation of the performance of clustering algorithms in kernel-induced feature space

By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However, few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results.     

Comparative analysis of different approaches to target differentiation and localization with sonar

This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-flight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications.     

A fast prototype reduction method based on template reduction and visualization-induced self-organizing map for nearest neighbor algorithm

The k nearest neighbor is a lazy learning algorithm that is inefficient in the classification phase because it needs to compare the query sample with all training samples. A template reduction method is recently proposed that uses only samples near the decision boundary for classification and removes those far from the decision boundary. However, when class distributions overlap, more border samples are retrained and it leads to inefficient performance in the classification phase. Because the number of reduced samples are limited, using an appropriate feature reduction method seems a logical choice to improve classification time. This paper proposes a new prototype reduction method for the k nearest neighbor algorithm, and it is based on template reduction and ViSOM. The potential property of ViSOM is displaying the topology of data on a two-dimensional feature map, it provides an intuitive way for users to observe and analyze data. An efficient classification framework is then presented, which combines the feature reduction method and the prototype selection algorithm. It needs a very small data size for classification while keeping recognition rate. In the experiments, both of synthetic and real datasets are used to evaluate the performance. Experimental results demonstrate that the proposed method obtains above 70 % speedup ratio and 90 % compression ratio while maintaining similar performance to kNN.     

Computing all efficient solutions of the biobjective minimum spanning tree problem

A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions. The second phase, the computation of non-extreme solutions, can be based on a “k-best” algorithm for the single-objective version of the problem or on the branch-and-bound method. A k-best algorithm computes the k-best solutions in order of their objective values. We compare the performance of these two approaches applied to the biobjective minimum spanning tree problem. Our extensive computational experiments indicate the overwhelming superiority of the k-best approach. We propose heuristic enhancements to this approach which further improve its performance.     

A classifier learning system using a coevolution method for deflection yoke misconvergence pattern classification problem

Deflection yoke (DY) is one of the core components of a cathode ray tube (CRT) in a computer monitor or a television that determines the image quality. Once a DY anomaly is found from beam patterns on a display in the production line of CRTs, the remedy process should be performed through three steps: identifying misconvergence types from the anomalous display pattern, adjusting manufacturing process parameters, and fine tuning. This study focuses on discovering a classifier for the identification of DY misconvergence patterns by applying a coevolutionary classification method. The DY misconvergence classification problems may be decomposed into two subproblems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two subproblems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional feature set, and then fits a finite number of classifiers to the data pattern by using a genetic algorithm in every sub-region. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding sub-regions. The classifier system has been tested with real-field data acquired from the production line of a computer monitor manufacturer in Korea, showing superior performance to other methods such as k-nearest neighbors, decision trees, and neural networks.