Energy-based temporal neural networks for imputing missing values

Imputing missing values in high dimensional time series is a difficult problem. There have been some approaches to the problem [11,8] where neural architectures were trained as probabilistic models of the data. However, we argue that this approach is not optimal. We propose to view temporal neural networks with latent variables as energy-based models and train them for missing value recovery directly. In this paper we introduce two energy-based models. The first model is based on a one dimensional convolution and the second model utilizes a recurrent neural network. We demonstrate how ideas from the energy-based learning framework can be used to train these models to recover missing values. The models are evaluated on a motion capture dataset

Missing Value Imputation Using Stratified Supervised Learning for Cardiovascular Data

Legacy (and current) medical datasets are rich source of information and knowledge. However, the use of most legacy medical datasets is beset with problems. One of the most often faced is the problem of missing data, often due to oversights in data capture or data entry procedures. Algorithms commonly used in the analysis of data often depend on a complete data set. Missing value imputation offers a solution to this problem. This may result in the generation of synthetic data, with artificially induced missing values, but simply removing the incomplete data records often produces the best classifier results. With legacy data, simply removing the records from the original datasets can significantly reduce the data volume and often affect the class balance of the dataset. A suitable method for missing value imputation is very much needed to produce good quality datasets for better analysing data resulting from clinical trials. This paper proposes a framework for missing value imputation using stratified machine learning methods. We explore machine learning technique to predict missing value for incomplete clinical (cardiovascular) data, with experiments comparing this with other standard methods. Two machine learning (classifier) algorithms, fuzzy unordered rule induction algorithm and decision tree, plus other machine learning algorithms (for comparison purposes) are used to train on complete data and subsequently predict missing values for incomplete data. The complete datasets are classified using decision tree, neural network, K-NN and K-Mean clustering. The classification performances are evaluated using sensitivity, specificity, accuracy, positive predictive value and negative predictive value. The results show that final classifier performance can be significantly improved for all class labels when stratification was used with fuzzy unordered rule induction algorithm to predict missing attribute values

A New Paradigm for Development of Data Imputation Approach for Missing Value Estimation

Many real-world applications encountered a common issue in data analysis is the presence of missing data value and challenging task in many applications such as wireless sensor networks, medical applications and psychological domain and others. Learning and prediction in the presence of missing value can be treacherous in machine learning, data mining and statistical analysis. A missing value can signify important information about dataset in the mining process. Handling missing data value is a challenging task for the data mining process. In this paper, we propose new paradigm for the development of data imputation method for missing data value estimation based on centroids and the nearest neighbours. Firstly, identify clusters based on the k-means algorithm and calculate centroids and the nearest neighbour data records. Secondly, the nearest distances from complete dataset as well as incomplete dataset from the centroids and estimated the nearest data record which tends to be curse dimensionality. Finally, impute the missing value based nearest neighbour record using statistical measure called z-score. The experimental study demonstrates strengthen of the proposed paradigm for the imputation of the missing data value estimation in dataset. Tests have been run using different types of datasets in order to validate our approach and compare the results with other imputation methods such as KNNI, SVMI, WKNNI, KMI and FKNNI. The proposed approach is geared towards maximizing the utility of imputation with respect to missing data value estimation