Andrea Edwards

Sparse online learning and cost-sensitive learning are two important areas of machine learning and data mining research. Each has been well studied with many interesting algorithms developed. However, very limited published work addresses the joint study of these two fields. In this paper, to tackle the high-dimensional data streams with skewed distributions, we introduce a framework of cost-sensitive sparse online learning. Our proposed framework is a substantial extension of the influential Truncated Gradient (TG) method by formulating a new convex optimization problem, where the two mutual restraint factors, misclassification cost and sparsity, can be simultaneously and favorably balanced. We theoretically analyze the regret and cost bounds of the proposed algorithm, and pinpoint its theoretical merit compared to the existing related approaches. Large-scale empirical comparisons to five baseline methods on eight real-world streaming datasets demonstrate the encouraging performance of the developed method. Algorithm implementation and datasets are available upon request.

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS Forestto systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.

Data stream classification and imbalanced data learning are two important areas of data mining research. Each has been well studied to date with many interesting algorithms developed. However, only a few approaches reported in literature address the intersection of these two fields due to their complex interplay. In this work, we proposed an importance sampling driven, dynamic feature group weighting framework (DFGW-IS) for classifying data streams of imbalanced distribution. Two components are tightly incorporated into the proposed approach to address the intrinsic characteristics of concept-drifting, imbalanced streaming data. Specifically, the ever-evolving concepts are tackled by a weighted ensemble trained on a set of feature groups with each sub-classifier (i.e. a single classifier or an ensemble) weighed by its discriminative power and stable level. The un-even class distribution, on the other hand, is typically battled by the sub-classifier built in a specific feature group with the underlying distribution rebalanced by the importance sampling technique. We derived the theoretical upper bound for the generalization error of the proposed algorithm. We also studied the empirical performance of our method on a set of benchmark synthetic and real world data, and significant improvement has been achieved over the competing algorithms in terms of standard evaluation metrics and parallel running time. Algorithm implementations and datasets are available upon request.

The exonization of transposable elements (TEs) has proven to be a significant mechanism for the creation of novel exons. Existing knowledge of the retention patterns of TE exons in mRNAs were mainly established by the analysis of Expressed Sequence Tag (EST) data and microarray data.

Alu elements are the most abundant retrotransposable elements comprising ~11% of the human genome. Many studies have highlighted the role that Alu elements have in genetic instability and how their contribution to the assortment of mutagenic events can lead to cancer. As of yet, little has been done to quantitatively assess the association between Alu distribution and genes that are causally implicated in oncogenesis.

Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.

Insertion site characterisation of Alu elements is an important problem in primate-specific bioinformatics research. Key characteristics of this challenging problem include: data are not in the pre-defined feature vectors for predictive model construction; without any prior knowledge, can we discover the general patterns that could exist and also make biological insights?; how to obtain the compact yet discriminative patterns given a search space of 4