A robust missing value imputation method for noisy data

Missing data imputation is an important research topic in data mining. The impact of noise is seldom considered in previous works while real-world data often contain much noise. In this paper, we systematically investigate the impact of noise on imputation methods and propose a new imputation approach by introducing the mechanism of Group Method of Data Handling (GMDH) to deal with incomplete data with noise. The performance of four commonly used imputation methods is compared with ours, called RIBG (robust imputation based on GMDH), on nine benchmark datasets. The experimental result demonstrates that noise has a great impact on the effectiveness of imputation techniques and our method RIBG is more robust to noise than the other four imputation methods used as benchmark.     

The effect of skill level,external frame of reference and environmental changes on one-handed catching

Abstract

Two groups of subjects designated ‘good’ (n= 12) and ‘poor’ (n= 10) catchers on the basis of a task-specific criterion were required to catch, one-handed, balls projected from a ball-projection machine under eight different conditions. Four of these comprised the ‘environment illuminated’ conditions (full light; hand obscured by an opaque screen; a see-through screen and an external vertical reference frame in the field of view) and four others the ‘environment dark’ conditions (catching hand wearing a glove painted with luminescent paint and u. v. illuminated ball in an otherwise dark room; ball illuminated by u. v. light; luminescent hand and external luminescent vertical reference plus u. v. illuminated ball; u. v.-illuminated ball, plus luminescent vertical reference).

While clear and significant differences - as was to be expected from the selection criteria imposed - were shown between the groups of ‘good’ and ‘poor’ catchers for all three dependent variables-number of catches, spatial errors and temporal errors-it was demonstrated that, for both groups, degrading the environment, by reducing the information available, resulted in a significant performance decrement on the dependent variables ‘number of catches’ and ‘temporal errors’. ‘Poor’ catchers were also shown to make significantly more spatial errors than ‘good’ catchers. Whereas the latter finding confirms earlier findings reported in the literature, the significant effect on performance of degrading the environment -particularly with respect to temporal errors - is new. The finding is discussed in the framework of the use of ‘tau’ (time to contact) in making judgements about when to initiate the grasp response.     

Cluster ensemble framework based on the group method of data handling

Cluster ensemble is a powerful method for improving both the robustness and the stability of unsupervised classification solutions. This paper introduced group method of data handling (GMDH) to cluster ensemble, and proposed a new cluster ensemble framework, which named cluster ensemble framework based on the group method of data handling (CE-GMDH). CE-GMDH consists of three components: an initial solution, a transfer function and an external criterion. Several CE-GMDH models can be built according to different types of transfer functions and external criteria. In this study, three novel models were proposed based on different transfer functions: least squares approach, cluster-based similarity partitioning algorithm and semidefinite programming. The performance of CE-GMDH was compared among different transfer functions, and with some state-of-the-art cluster ensemble algorithms and cluster ensemble frameworks on synthetic and real datasets. Experimental results demonstrate that CE-GMDH can improve the performance of cluster ensemble algorithms which used as the transfer functions through its unique modelling process. It also indicates that CE-GMDH achieves a better or comparable result than the other cluster ensemble algorithms and cluster ensemble frameworks.     

Application of pseudolinear partitioned filter to passive vehicle tracking†

The partitioned estimation method is applied to the bearing-only target tracking problem. A pseudolinear partitioned tracking filter is developed initially in the form of recursive processing. It is then shown that such a tracking filter consists mainly of two parts, a ‘ manoeuvre predictor ’, driven by the deterministic own-sensor manoeuvre input, and a ‘ psuedolinear partitioning fixed-point smoother ’, which gives the initial position and speed estimates. Furthermore, by taking into consideration the parallel processing mechanism, a pseudolinear partitioned tracking filter with data compression is proposed to average the bearing data contaminated by the measurement noise. The parametric relationship between r.m.s. estimation error, data compressing (or renovating) interval, measurement noise level, sensor manoeuvre structure and initial range estimate is presented through Monte Carlo simulations.     

Deep feedback GMDH-type neural network and its application to medical image analysis of MRI brain images

The deep feedback group method of data handling (GMDH)-type neural network is applied to the medical image analysis of MRI brain images. In this algorithm, the complexity of the neural network is increased gradually using the feedback loop calculations. The deep neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image analysis of MRI brain images, because the optimum neural network architectures fitting the complexity of the medical images are automatically organized so as to minimize the prediction error criterion defined as AIC or PSS.     

轻度认知障碍的BOLD信号MEMD降噪处理

早期诊断轻度认知障碍是干预阿尔茨海默症的有效途径.目前常使用静息态功能磁共振成像和机器学习方法进行轻度认知障碍的辅助诊断,其关键是使用血氧水平依赖(blood oxygenation level dependent, BOLD)信号构建大脑的功能性连接.针对大脑静息态BOLD信号中存在各种外界噪音干扰的问题,提出结合多元经验模态分解与皮尔逊相关的重构方法与极正极负重构准则,将大脑默认模式网络的中心节点后扣带回皮层作为模板,重构BOLD信号以降低外界噪音干扰.实验结果表明,基于极正极负重构准则降噪后的BOLD信号构建功能性连接,相较降噪前的数据,在分类性能方面可以提高数据的差异性,在特征选择性能方面可以对数据集降维的同时进一步提升分类性能.此外,以上性能均优于传统重构准则.最后,对降噪后的最优特征子集进行统计性分析,发现脑岛可能是默认模式网络的相关脑区,小脑蚓体与后扣带回皮层可能构成一种认知功能补偿网络,这是以往研究中少有提出的结论.     

The k-nearest neighbour-based GMDH prediction model and its applications

This paper centres on a new GMDH (group method of data handling) algorithm based on the k-nearest neighbour (k-NN) method. Instead of the transfer function that has been used in traditional GMDH, the k-NN kernel function is adopted in the proposed GMDH to characterise relationships between the input and output variables. The proposed method combines the advantages of the k-nearest neighbour (k-NN) algorithm and GMDH algorithm, and thus improves the predictive capability of the GMDH algorithm. It has been proved that when the bandwidth of the kernel is less than a certain constant C, the predictive capability of the new model is superior to that of the traditional one. As an illustration, it is shown that the new method can accurately forecast consumer price index (CPI).     

Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer

In this study, the deep multi-layered group method of data handling (GMDH)-type neural network algorithm using revised heuristic self-organization method is proposed and applied to medical image diagnosis of liver cancer. The deep GMDH-type neural network can automatically organize the deep neural network architecture which has many hidden layers. The structural parameters such as the number of hidden layers, the number of neurons in hidden layers and useful input variables are automatically selected to minimize prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The architecture of the deep neural network is automatically organized using the revised heuristic self-organization method which is a type of the evolutionary computation. This new neural network algorithm is applied to the medical image diagnosis of the liver cancer and the recognition results are compared with the conventional 3-layered sigmoid function neural network.     

Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network

In this study, a deep multi-layered group method of data handling (GMDH)-type neural network is applied to the medical image analysis of the abdominal X-ray computed tomography (CT) images. The deep neural network architecture which has many hidden layers are automatically organized using the deep multi-layered GMDH-type neural network algorithm so as to minimize the prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The characteristics of the medical images are very complex and therefore the deep neural network architecture is very useful for the medical image diagnosis and medical image recognition. In this study, it is shown that this deep multi-layered GMDH-type neural network is useful for the medical image analysis of abdominal X-ray CT images.     

Including probabilistic target detection attributes into map representations

Range measuring sensors can play an extremely important role in robot navigation. All range measuring devices rely on a ‘detection criterion’ made in the presence of noise, to determine when the transmitted signal is considered detected and hence a range reading is obtained. In commonly used sensors, such as laser range finders and polaroid sonars, the criterion under which successful detection is assumed, is kept hidden from the user. However, ‘detection decisions’ on the presence of noise still take place within the sensor. This paper integrates signal detection probabilities into the map building process which provides the most accurate interpretation of such sensor data. To facilitate range detection analysis, map building with a frequency modulated continuous wave millimetre wave radar (FMCW MMWR), which is able to provide complete received power-range spectra for multiple targets down range is considered. This allows user intervention in the detection process and although not directly applicable to the commonly used ‘black-box’ type range sensors, provides insight as to how not only range values, but received signal strength values should be incorporated into the map building process.This paper presents two separate methods of map building with sensors which return both range and received signal power information. The first is an algorithm which uses received signal-to-noise power to make an estimates of the range to multiple targets down range, without any signal distribution assumptions. We refer to this as feature detection based on target presence probability (TPP). In contrast to the first method, the second method does use assumptions on the statistics of the signal in target presence and absence scenarios to formulate a probabilistic likelihood detector. This allows for an increased rate of convergence to ground truth. Evidence theory is then introduced to model and update successive observations in a recursive fashion. Both methods are then compared using real MMWR data sets from indoor and outdoor experiments.     

Effects of noise on signal detection

Twelve subjects were tested twice in visual vigilance tasks which lasted 40?min. Employing a two-category confidence rating scale they detected increments in light level from displays of five lights. The display was flashed on simultaneously for 0·5 s every 3·5?s. The subjects performed the task on different days under two conditions of continuous white noise: ‘quiet’ (70?dB) and ‘noise’ (l00?dB). Half of the subjects had the noise treatment in the order of quiet-noise and half in the reverse order. No effects of noise either upon the overall performance or upon the vigilance decrement were observed. For the risky criterion results showed mainly that during a run under the two conditions the percentage of correct and false responses decreased, d' remained unchanged and β partly increased as a function of time. For the cautious criterion only β increased during a run under the two conditions. The results were interpreted in terms of arousal theory     

Heuristics free group method of data handling algorithm of generating optimal partial polynomials with application to air pollution prediction†

In this paper a revised GMDH (Group Method of Data Handling) algorithm is developed in which heuristicsare not required such as dividing the available date. into training data and checking data, predetermining the structure of the partial polynomials, or predetermining the number of intermediate variables. In this algorithm the prediction error criterion, such as PSS (Prediction Sum of Squares) or AIC (Akaike's Information Criterion) evaluated from all the available data, in used as a criterion for generating optimal partial polynomials, for selecting intermediate variables and for stopping the multilayered iterative computation. This heuristics freeGMDH algorithm is applied to non-linear modelling for short-term prediction of air pollution concentration. By using the time series data of SO₂, concentration, the wind velocity and the wind direction in Tokushima; Japan, a suitable model for predicting SO₂concentration at a few hours in advance is developed. The predicted results obtained by the revised GMDH model are compared with the results obtained by a linear regression model, a linear autoregressive model and a. basic GMDH model.     

Three-dimensional medical image analysis of the heart by the revised GMDH-type neural network self-selecting optimum neural network architecture

In this study, a revised group method of data handling (GMDH)-type neural network algorithm which self-selects the optimum neural network architecture is applied to 3-dimensional medical image analysis of the heart. The GMDH-type neural network can automatically organize the neural network architecture by using the heuristic self-organization method, which is the basic theory of the GMDH algorism. The heuristic self-organization method is a kind of evolutionary computation method. In this revised GMDH-type neural network algorithm, the optimum neural network architecture was automatically organized using the polynomial and sigmoid function neurons. Furthermore, the structural parameters, such as the number of layers, the number of neurons in the hidden layers, and the useful input variables, are selected automatically in order to minimize the prediction error criterion, defined as the prediction sum of squares (PSS).     

Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

Motion estimation on ultrasound data is often referred to as ‘Speckle Tracking’ in clinical environments and plays an important role in diagnosis and monitoring of cardiovascular diseases and the identification of abnormal cardiac motion. The impact of physical effects in the process of data acquisition raises many problems for conventional image processing techniques. The most significant difference to other medical data is its high level of speckle noise, which has completely different characteristics from other noise models, e.g., additive Gaussian noise. In this paper we address the problem of multiplicative speckle noise for motion estimation techniques that are based on optical flow methods and prove that the influence of this noise leads to wrong correspondences between image regions if not taken into account. To overcome these problems we propose the use of local statistics and introduce an optical flow method which uses histograms as discrete representations of local statistics for motion analysis. We show that this approach is more robust under the presence of speckle noise than classical optical flow methods.     

数据组合处理方法在数据预测中的应用

数据组合处理方法（GMDH）是20世纪70年代发展起来的一种启发式自组织建立模型的方法;它能充分地、合理地利用数据,自动进行变量组合,筛选以及判断从而得到合适的模型;简单介绍了该方法建模的基本原理和算法实现,给出了仿真算例,并与用相同资料建立的PPR预测模型的预测结果进行了比较;仿真结果表明,用GMDH方法建立非线性系统模型,具有预测精度高、计算稳定性好等优点。     

A Modular Real-time Tidal Prediction Model based on Grey-GMDH Neural Network

Real-time prediction of tidal level is of great significance for activities of human beings in the fields of marine and coastal engineering. However, the disturbance factors of tidal level are very intricate, which deteriorate the tidal prediction accuracy. To improve the accuracy of real-time tidal-level prediction, a modular real-time tidal-level prediction approach is proposed based on the grey group method of data handling (Grey-GMDH) neural network. The modular model is composed of astronomical tide parts caused by celestial bodies’ movement and the nonastronomical tide parts caused by various meteorological and other environmental factors. The GMDH is a polynomial network that is commonly used in prediction and pattern recognition. However, GMDH is sensitive to nondeterministic time series, which would result in low accuracy of prediction. In this study, the grey prediction theory is introduced into the GMDH prediction model to alleviate the unfavorable effects of uncertainty caused by various environmental factors and the adverse effects caused thereby on the prediction accuracy. In this study of tidal prediction, the Grey-GMDH model is used to predict the nonastronomical tide parts, whereas the conventional harmonic analysis model is used to predict the astronomical tide parts. The final prediction result is achieved by combining the estimation outputs of the harmonious analysis model and the Grey-GMDH model. Measured tidal-level data of San Diego tidal station is selected as the testing database. Simulation and experimental results confirm that the proposed approach can achieve real-time predictions for tidal level with high accuracy, satisfactory convergence and stability.     

Adaptation in Observers During Television Quality-Grading Tests I. Adaptation as a Function of the Conditioning Situation

The results of subjective quality-grading tests aro affected by ‘ adaptation ’ in the sense that observers become more critical when the quality of the tost material is generally high, and vice versa- The phenomenon has been investigated for tests with television pictures impaired by random noise and by a long-delayed echo. The magnitude of the effect of adaptation as a function of the ‘ conditioning situation ’ is described in terms of ‘ adaptation coefficients ’; it is greater for random noise.     

基于遗传算法的GMDH网络模型及其应用

传统的数据处理群方法(Group method of data handling,GMDH)在结构上具有自组织和全局选优的特性,非常适合进行非线性数据的拟合.但由于在传统GMDH网络建模是用最小二乘法来辨识参数,常常使得模型预测效果不理想.遗传算法是一种有效的搜索和优化方法.它具有自适应搜索、渐进式寻优、并行式搜索、通用性强等特点,论文将遗传算法引入GMDH网络,用遗传算法辨识部分描述式的系数,建立了基于遗传算法的GMDH网络模型.并将该模型应用于一组实测时间序列的预测研究.计算机仿真结果表明,模型预测效果令人满意.     

Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication

In this paper, the fuzzy group method data handling-type (GMDH) neural networks and their application to the forecasting of mobile communication systems are described. At present, the GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neural-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called the neural-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. A computer program is developed and successful applications are shown in the field of estimating problems of mobile communication with a number of factors considered.     

Problems of complex system modeling and applied mathematical statistics

Basic problems in the use of applied mathematical statistics for the modeling of complex systems are considered; the possibility of establishing the uniqueness of a mathematical model of optimal complexity by the group method of data handling (GMDH) is demonstrated. The basic shortcoming of contemporary mathematical statistics is that the models used are too simple because until now in regression analysis only one mean-squared error criterion has been used. To define a mathematical model of optimal complexity GMDH uses not one but two criteria and these two criteria assure a unique solution. The resulting equations are so complex that only the multilayered structure of GMDH allows us to write them down. The method works not only whenK N but also whenK >N(Kis the number of coefficients of the regression equation,N is the number of interpolation points). Increasing the area of optimization raises the accuracy of the model. The second criterion should be heuristic. Mean-squared error defined on a test sequence is used. The division of data into training and test sequences is the basic object of so-called goal-directed regularization. A second shortcoming of contemporary applied mathematical statistics is the absence of freedom of decision in the terminology of D. Gabor. The GMDH selection-type algorithm realizes both the self-organization and freedom of decision criteria. GMDH is a nonparametric procedure and does not require many of the concepts of mathematical statistics.