High-quality Training Datasets Research Articles

An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems

Provision of training data sets is one of the core requirements for event-based supervised NILM (Non-Intrusive Load Monitoring) algorithms. Due to diversity in appliances’ technologies, in-situ training by users is often required. This process might require continuous user-interaction to ensure that a high quality training data set is provided. Pre-populating a training data set could potentially reduce the need for user-system interaction. In this study, a heuristic unsupervised clustering algorithm is presented and evaluated to enable autonomous partitioning of appliances signature space (i.e. feature space) for applications in electricity consumption disaggregation. The algorithm is based on hierarchical clustering and uses the characteristics of a cluster binary tree to determine the distance threshold for pruning the tree without a priori information. The algorithm determines the partition of a feature space recursively to account for multi-scale nature of the binary cluster tree. Evaluation of the algorithm was carried out using metrics for accuracy and cluster quality (proposed in this study) on a fully labeled data set that was collected and processed in a real residential setting. The algorithm performance in accurate partitioning of the feature space and the effect of different feature extraction techniques were presented and discussed.

Neural network Jacobian analysis for high-resolution profiling of the atmosphere

Neural networks have been widely used to provide retrievals of geophysical parameters from spectral radiance measurements made remotely by air-, ground-, and space-based sensors. The advantages of retrievals based on neural networks include speed of execution, simplicity of the trained algorithm, and ease of error analysis, and the proliferation of high quality training data sets derived from models and/or operational measurements has further facilitated their use. In this article, we provide examples of geophysical retrieval algorithms based on neural networks with a focus on Jacobian analysis. We examine a hypothetical 80-channel hyperspectral microwave atmospheric sounder (HyMAS) and construct examples comparing neural network water vapor retrieval performance with simple regressions. Jacobians (derivatives of the outputs with respect to the network weights and with respect to the inputs) are also presented and discussed. Finally, a discussion of the Jacobian operating points is provided.

A novel data selection method based on shadowed sets

One of the main factors that affect the supervised learning performance of neural network (NN) is training data set. However, for real world problems, it is not always easy to obtain high quality training data sets. In this paper, a novel training data selection method based on shadowed sets is proposed that can select an informative and representative subset of training data to ameliorate the supervised learning performance of NN. The main goal of this work is to improve the generalization ability and diminish misclassification errors of classifier of NN. This paper firstly introduces central idea of shadowed sets. Then the algorithm of proposed data selection method is described in detail. Finally, taking LVQ model as an example, some experiments are made to test validity of this method. The experiment results indicate that using selected sample data to train NN can lessen computation consumption, save training time, guarantee generalization ability, which verify the effectiveness and applicability of proposed data selection method.

Learning from imperfect data

For a supervised learning method, the quality of the training data or the training supervisor is very important in generating reliable neural networks. However, for real world problems, it is not always easy to obtain high quality training data sets. In this research, we propose a learning method for a neural network ensemble model that can be trained with an imperfect training data set, which is a data set containing erroneous training samples. With a competitive training mechanism, the ensemble is able to exclude erroneous samples from the training process, thus generating a reliable neural network. Through the experiment, we show that the proposed model is able to tolerate the existence of erroneous training samples in generating a reliable neural network. The ability of the neural network to tolerate the existence of erroneous samples in the training data lessens the costly task of analyzing and arranging the training data, thus increasing the usability of the neural networks for real world problems.