A novel dynamic multi-criteria ensemble selection mechanism applied to drinking water quality anomaly detection.

The provision of clean and safe drinking water is a crucial task for water supply companies from all over the world. To this end, automatic anomaly detection plays a critical role in drinking water quality monitoring. Recent anomaly detection studies use techniques that focus on a single global objective. Yet, companies need solutions that better balance the trade-off between false positives (FPs), which lead to financial losses to water companies, and false negatives (FNs), which severely impact public health and damage the environment. This work proposes a novel dynamic multi-criteria ensemble selection mechanism to cope with both problems simultaneously: the non-dominated local class-specific accuracy (NLCA). Moreover, experiments rely on recent time series related classification metrics to assess the predictive performance. Results on data from a real-world water distribution system show that NLCA outperforms other ensemble learning and dynamic ensemble selection techniques by more than 15% in terms of time series related F1 scores. As a conclusion, NLCA enables the development of stronger anomaly detection systems for drinking water quality monitoring. The proposed technique also offers a new perspective on dynamic ensemble selection, which can be applied to different classification tasks to balance conflicting criteria.

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case.

BACKGROUND: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. RESULTS: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. CONCLUSION: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.