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Automatic composition and parametrisation of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps is a challenging task. In this paper we propose and describe an extension to the Auto-WEKA software which now allows to compose and optimise such flexible MCPSs by using a sequence of WEKA methods. In the experimental analysis we focus on examining the impact of significantly extending the search space by incorporating additional hyperparameters of the models, on the quality of the found solutions. In a range of extensive experiments three different optimisation strategies are used to automatically compose MCPSs on 21 publicly available datasets. A comparison with previous work indicates that extending the search space improves the classification accuracy in the majority of the cases. The diversity of the found MCPSs are also an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. This can have a big impact on high quality predictive models development, maintenance and scalability aspects needed in modern application and deployment scenarios.

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Quick presentation for the OpenML workshop in Eindhoven 2014

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In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.

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The requirements for analysing big volumes of data have increased over the last few decades. The process of selecting, cleaning, modelling and interpreting data is called the KDD process. The decision of how to approach each step in this process has often been made manually by experts. However, experts cannot be aware of all methods, nor is it feasible to try all of them. Researchers have proposed different approaches for automating, or at least advising, the stages of the KDD process. This talk will outline the different types of Intelligent Discovery Assistants as described in the work of Serban et al. “A survey of intelligent assistants for data analysis” and point out some future directions.

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From sensor readings to prediction: on the process of developing practical so...

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