This research estimates the optimal number of clusters in a dataset using a novel ensemble technique - a preferred alternative to relying on the output of a single clustering. Combining clusterings from different algorithms can lead to a more stable and robust solution, often unattainable by any single clustering solution. Technically, we created subsets of ensembles as possible estimates; and evaluated them using a quality metric to obtain the best subset. We tested our method on publicly available datasets of varying types, sources and clustering difficulty to establish the accuracy and performance of our approach against eight standard methods. Our method outperforms all the techniques in the number of clusters estimated correctly. Due to the exhaustive nature of the initial algorithm, it is slow as the number of ensembles or the solution space increases; hence, we have provided an updated version based on the single-digit difference of Gray code that runs in linear time in terms of the subset size.

Multi-label classification in deep learning is a practical yet challenging task, because class overlaps in the feature space means that each instance is associated with multiple class labels. This requires a prediction of more than one class category for each input instance. To the best of our knowledge, this is the first deep learning study which quantifies uncertainty and model interpretability in multi-label classification; as well as applying it to the problem of recognising proteins expressed in cell types in testes based on immunohistochemically stained images. Multi-label classification is achieved by thresholding the class probabilities, with the optimal thresholds adaptively determined by a grid search scheme based on Matthews correlation coefficients. We adopt MC-Dropweights to approximate Bayesian Inference in multi-label classification to evaluate the usefulness of estimating uncertainty with predictive score to avoid overconfident, incorrect predictions in decision making. Our experimental results show that the MC-Dropweights visibly improve the performance to estimate uncertainty compared to state of the art approaches.

Many image analysis tasks involve the automatic segmentation and counting of objects with specific characteristics. However, we find that current approaches look to either segment objects or count them through bounding boxes, and those methodologies that both segment and count struggle with co-located and overlapping objects. This restricts our capabilities when, for example, we require the area covered by particular objects as well as the number of those objects present, especially when we have a large amount of images to obtain this information for. In this paper, we address this by proposing a Dual-Output U-Net. DO-U-Net is an Encoder-Decoder style, Fully Convolutional Network (FCN) for object segmentation and counting in image processing. Our proposed architecture achieves precision and sensitivity superior to other, similar models by producing two target outputs: a segmentation mask and an edge mask. Two case studies are used to demonstrate the capabilities of DO-U-Net: locating and counting Internally Displaced People (IDP) tents in satellite imagery, and the segmentation and counting of erythrocytes in blood smears. The model was demonstrated to work with a relatively small training dataset, achieving a sensitivity of 98.69% for IDP camps of the fixed resolution, and 94.66% for a scale-invariant IDP model. DO-U-Net achieved a sensitivity of 99.07% on the erythrocytes dataset. DO-U-Net has a reduced memory footprint, allowing for training and deployment on a machine with a lower to mid-range GPU, making it accessible to a wider audience, including non-governmental organisations (NGOs) providing humanitarian aid, as well as health care organisations.

In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.