A comparison of imputation methods for handling missing scores in biometric fusion

Multibiometric systems, which consolidate or fuse multiple sources of biometric information, typically provide better recognition performance than unimodal systems. While fusion can be accomplished at various levels in a multibiometric system, score-level fusion is commonly used as it offers a good trade-off between data availability and ease of fusion. Most score-level fusion rules assume that the scores pertaining to all the matchers are available prior to fusion. Thus, they are not well equipped to deal with the problem of missing match scores. While there are several techniques for handling missing data in general, the imputation scheme, which replaces missing values with predicted values, is preferred since this scheme can be followed by a standard fusion scheme designed for complete data. In this work, the performance of the following imputation methods are compared in the context of multibiometric fusion: K-nearest neighbor (KNN) schemes, likelihood-based schemes, Bayesian-based schemes and multiple imputation (MI) schemes. Experiments on the MSU database assess the robustness of the schemes in handling missing scores at different missing rates. It is observed that the Gaussian mixture model (GMM)-based KNN imputation scheme results in the best recognition accuracy.     

Ensemble learning for wind profile prediction with missing values

In this paper, we aim to develop computational intelligence approaches for wind profile prediction. Specifically, we focus on two aspects in this work. First, we investigate the missing value recovery for wind data. Due to the complexity of data collection in such processes, wind data normally include missing values. Therefore, how to effectively recover such missing values for learning and prediction is an important aspect for wind profile prediction. Second, we develop an ensemble learning approach based on multiple neural network models. Our proposed method uses a new strategy based on the temporal information to assign the weights for each model dedicated for wind profile prediction to achieve better prediction performance. Various simulation studies and statistical testing demonstrate the effectiveness of our approach.     

Empirical comparison of supervised learning techniques for missing value imputation

Knowledge and Information Systems - Many data mining algorithms cannot handle incomplete datasets where some data samples are missing attribute values. To solve this problem, missing value...     

Improving deep learning performance with missing values via deletion and compensation

Neural Computing and Applications - Missing values in a dataset is one of the most common difficulties in real applications. Many different techniques based on machine learning have been proposed...     

一种无参数的微阵列缺失值填补方法

微阵列数据中的缺失值会对随后的数据分析造成影响。因此,正确地估计这些缺失值是很必要的。将一个k值选取算法结合到有序的局部最小二乘填补算法中,提出了一种无参数的缺失值填补方法(SLLSkimpute)。该方法的三个特点是:第一,无需事先确定参数;第二,针对不同的目标基因使用不同数目的邻居基因;第三,有序地估计缺失值,并有选择地将已得到的估计值应用到后续的估计过程中。实验结果证实了该算法的有效性,其估计性能优于其它一些常用的填补方法。     

Principal component analysis of systemic lupus erythematosus (SLE): A proposal for handling data with many missing values

Data from 383 Japanese patients with systemic lupus erythematosus (SLE), collected by the schedule method, were analyzed by the principal component analysis. The new method, “combination of variates,” reduced the number of missing data. Quantitative indices useful in evaluating the clinical status of the patients were extracted. The first principal component correlated with the prognosis. Eighty-four patients with SLE were classified into four subgroups according to the second and third principal components. The subgroups were compared with 23 patients with progressive systemic sclerosis or dermatomyositis.     

A new adaptive learning rule

A method is presented for nonlinear function identification and application to learning control. The control objective is to identify and compensate for a nonlinear disturbance function. The nonlinear disturbance function is represented as an integral of a predefined kernel function multiplied by an unknown influence function. Sufficient conditions for the existence of such a representation are provided. Similarly, the nonlinear function estimate is generated by an integral of the predefined kernel multiplied by an influence function estimate. Using the time history of the plant, the learning rule indirectly estimates the unknown function by updating the influence function estimate. It is shown that the estimate function converges to the actual disturbance asymptotically. Consequently, the controller achieves the disturbance cancellation asymptotically. The method is extended to repetitive control applications. It is applied to the control of robot manipulators. Simulation and actual real-time implementation results using the Berkeley/NSK robot arm show that the proposed learning algorithm is more robust and converges at a faster rate than conventional repetitive controllers     

Complete autoencoders for classification with missing values

Neural Computing and Applications - It has been demonstrated that modified denoising stacking autoencoders (MSDAEs) serve to implement high-performance missing value imputation schemes. On the...     

A comparison of learning strategies for biologically constrained development of gaze control on an iCub robot

Gaze control requires the coordination of movements of both eyes and head to fixate on a target. We present a biologically constrained architecture for gaze control and show how the relationships between the coupled sensorimotor systems can be learnt autonomously from scratch, allowing for adaptation as the system grows or changes. Infant studies suggest developmental learning strategies, which can be applied to sensorimotor learning in humanoid robots. We examine two strategies (sequential and synchronous) for the learning of eye and head coupled mappings, and give results from implementations on an iCub robot. The results show that the developmental approach can give fast, cumulative, on-line learning of coupled sensorimotor systems.     

An effective method for classification with missing values

Classification is one of the most important tasks in machine learning with a huge number of real-life applications. In many practical classification problems, the available information for making object classification is partial or incomplete because some attribute values can be missing due to various reasons. These missing values can significantly affect the efficacy of the classification model. So it is crucial to develop effective techniques to impute these missing values. A number of methods have been introduced for solving classification problem with missing values. However they have various problems. So, we introduce an effective method for imputing missing values using the correlation among the attributes. Other methods which consider correlation for imputing missing values works better either for categorical or numeric data, or designed for a particular application only. Moreover they will not work if all the records have at least one missing attribute. Our method, Model based Missing value Imputation using Correlation (MMIC), can effectively impute both categorical and numeric data. It uses an effective model based technique for filling the missing values attribute wise and reusing then effectively using the model. Extensive performance analyzes show that our proposed approach achieves high performance in imputing missing values and thus increases the efficacy of the classifier. The experimental results also show that our method outperforms various existing methods for handling missing data in classification.     

Selection-fusion approach for classification of datasets with missing values

This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values.     

A data mining approach considering missing values for the optimization of semiconductor-manufacturing processes

Due to the rapid development of information technologies, abundant data have become readily available. Data mining techniques have been used for process optimization in many manufacturing processes in automotive, LCD, semiconductor, and steel production, among others. However, a large amount of missing values occurs in the data set due to several causes (e.g., data discarded by gross measurement errors, measurement machine breakdown, routine maintenance, sampling inspection, and sensor failure), which frequently complicate the application of data mining to the data set. This study proposes a new procedure for optimizing processes called missing values-Patient Rule Induction Method (m-PRIM), which handles the missing-values problem systematically and yields considerable process improvement, even if a significant portion of the data set has missing values. A case study in a semiconductor manufacturing process is conducted to illustrate the proposed procedure.     

A feature construction approach for genetic iterative rule learning algorithm

This paper presents a proposal that introduces the use of feature construction in a fuzzy rule learning algorithm. This is done by means of the combination of two different approaches together with a new learning strategy. The first of these two approaches consists of using relations in the antecedent of fuzzy rules while the second one employs functions in the antecedent of that rules. Thus, the method we propose tries to integrate these two models so that, using a learning strategy that allows us to start learning more general rules and finish the process learning more specific ones, we are able to increase the amount of information extracted from the initial variables. The experimental results show that the proposed method obtains a good trade-off among accuracy, interpretability and time needed to get the model in relation to the rest of algorithms using feature construction involved in the comparison.     

A new mutation rule for evolutionary programming motivated frombackpropagation learning

Evolutionary programming is mainly characterized by two factors: the selection strategy and the mutation rule. This letter presents a new mutation rule that has the same form as the well-known backpropagation learning rule for neural networks. The proposed mutation rule assigns the best individual's fitness as the temporary target at each generation. The temporal error, the distance between the target and an individual at hand, is used to improve the exploration of the search space by guiding the direction of evolution. The momentum, i.e., the accumulated evolution information for the individual, speeds up convergence. The efficiency and robustness of the proposed algorithm are assessed on several benchmark test functions     

A learning scheme for a fuzzy k-NN rule

The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership-array W[l,m_R], where l and m_R denote the number of classes and the number of elements in the reference set X_R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimate by the leaving ‘leaving one out‘ method.     

A learning rule for local synaptic interactions between excitation and shunting inhibition

The basic requirement for direction selectivity is a nonlinear interaction between two different inputs in space-time. In some models, the interaction is hypothesized to occur between excitation and inhibition of the shunting type in the neuron's dendritic tree. How can the required spatial specificity be acquired in an unsupervised manner? We here propose an activity-based, local learning model that can account for direction selectivity in visual cortex based on such a local veto operation and that depends on synaptically induced changes in intracellular calcium concentration. Our biophysical simulations suggest that a model cell with our learning algorithm can develop direction selectivity organically after unsupervised training. The learning rule is also applicable to a neuron with multiple-direction-selective subunits and to a pair of cells with opposite-direction selectivities and is stable under different starting conditions, delays, and velocities.     

非确定性数据库中空值处理

尽管关系数据库有很多优势,但它缺乏一种处理非确定性数据的能力.目前,已经提出了几种将非确定性结合到关系数据库模型的方法,它们对关系数据库模型做了诸多扩展.但空值问题依旧存在,一些模型根本就没有考虑空值因素.这违背了非确定性数据库要更加真实地反应现实世界的初衷.为此,给出了一种非确定性数据库系统中空值处理方法,改进现有非确定性数据库模型中对空值处理不完善的情况.     

GBNN-填充缺失属性值算法

在数据挖掘和机器学习领域,缺失数据经常出现,本文结合灰色系统理论和最近邻理论,提出了一种新的缺失数据填充方法(简称为GBNN算法),在实验中对本算法和常见的最近邻算法从分类准确率和预测正确率两个方面进行了比较,分析了本算法的优越性。