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Many datasets are of increasingly high dimension- ality, where a large number of features could be irrelevant to the learning task. The inclusion of such features would not only introduce unwanted noise but also increase computational complexity. Deep neural networks (DNNs) outperform machine learning (ML) algorithms in a variety of applications due to their effectiveness in modelling complex problems and handling high-dimensional datasets. However, due to non-linearity and higher-order feature interactions, DNN models are unavoidably opaque, making them black-box methods. In contrast, an interpretable model can identify statistically significant features and explain the way they affect the model’s outcome. In this paper, we propose a novel method to improve the interpretability of blackbox models in the case of high-dimensional datasets. First, a black-box model is trained on full feature space that learns useful embeddings on which the classification is performed. To decompose the inner principles of the black-box and to identify top-k important features (global explainability), probing and perturbing techniques are applied. An interpretable surrogate model is then trained on top-k feature space to approximate the black-box. Finally, decision rules and counterfactuals are derived from the surrogate to provide local decisions. Our approach outperforms tabular learners, e.g., TabNet and XGboost, and SHAP-based interpretability techniques, when tested on a number of datasets having dimensionality between 54 and 20,5311.1GitHub: https://github.com/rezacsedu/DeepExplainHidim

Complex, multivariate data streams frequently comprise subjacent behavioral patterns, which are subsumable by a process of statistical structure discovery. Revealing these hidden patterns from raw data is a major challenge in abstracting information and thus for new opportunities of efficient data analysis at scale. State-of-the-art approaches, such as CKS and ABCD, leverage statistical data models and Gaussian Processes in order to abstract from raw data and to describe their major data characteristics by means of kernel-decomposed covariance functions. The process of identifying the most appropriate covariance function is a performance bottleneck due to its super-quadratic computation time complexity for model selection and evaluation. In this paper, we thus propose a new approach for the computation of large-scale statistical data models. To this end, we propose to bound the complexity of the statistical data model and develop a sequential agglomerative approach to reduce the computational load of the required evaluative calculations. Our performance analysis indicates that our proposal is able to outperform state-of-the-art kernel search algorithms such as CKS and ABCD with respect to the qualities of efficiency and accuracy.

Data are a central phenomenon in our digital information age. They impact the way we live, work, and play and provide unprecedented opportunities to simplify our daily life and behavior. They implicate enormous potential and impact society, economy, and science. Due to the advancement of cyber-physical systems and Internet of Things technologies, it is expected that the majority of real-time data will be generated from devices interconnected within the Internet of Things by the year 2025. In this paper, we tackle the problem of managing Internet of Things data in an efficient way. To this end, we introduce the metric approach for storing and querying Internet of Things data and investigate the ability of pivot-based tables for indexing and searching this type of data. Along with the introduction of two real-world, large-scale Internet of Things datasets from the EU projects COMPOSITION and MONSOON (under grant no. 723145 and 723650), we show that the metric approach facilitates efficient data access in the Internet of Things.

Accessing very large multimedia databases in a content-based way has become one of the major challenges in todays' multimedia analysis and retrieval applications. Accompanied by the heterogeneity of data and the continuous change of user requirements, content-based approaches are supposed to efficiently retrieve and analyze query-like multimedia objects with the highest possible degree of efficacy, which makes the utilization of complex multimedia object representations and adaptive similarity measures inevitable. In order to facilitate efficient and flexible content-based access into multimedia databases comprising millions of complex data objects, we propose the Multi-step Threshold Algorithm (MTA). Based on a set of query features, the MTA aims to retrieve the most similar multimedia objects with minimal I/O cost by incrementally traversing an in-memory index structure in a feature-by-feature manner in order to approximate the multimedia objects' similarities prior to database access. In addition to the MTA, we propose different enhancements that ensure scalable feature-based query processing. Our performance analysis evidences that our proposal is able to process feature-based queries on a million-scale multimedia database in milliseconds on a single CPU.