A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Assessment of metal ion concentration in water with structured feature selection
Tekijät: Naula P, Airola A, Pihlasalo S, Perez IM, Salakoski T, Pahikkala T
Kustantaja: PERGAMON-ELSEVIER SCIENCE LTD
Julkaisuvuosi: 2017
Journal: Chemosphere
Tietokannassa oleva lehden nimi: CHEMOSPHERE
Lehden akronyymi: CHEMOSPHERE
Vuosikerta: 185
Aloitussivu: 1063
Lopetussivu: 1071
Sivujen määrä: 9
ISSN: 0045-6535
DOI: https://doi.org/10.1016/j.chemosphere.2017.07.079
Tiivistelmä
We propose a cost-effective system for the determination of metal ion concentration in water, addressing a central issue in water resources management. The system combines novel luminometric label array technology with a machine learning algorithm that selects a minimal number of array reagents (modulators) and liquid sample dilutions, such that enable accurate quantification. The algorithm is able to identify the optimal modulators and sample dilutions leading to cost reductions since less manual labour and resources are needed. Inferring the ion detector involves a unique type of a structured feature selection problem, which we formalize in this paper. We propose a novel Cartesian greedy forward feature selection algorithm for solving the problem. The novel algorithm was evaluated in the concentration assessment of five metal ions and the performance was compared to two known feature selection approaches. The results demonstrate that the proposed system can assist in lowering the costs with minimal loss in accuracy. (c) 2017 Elsevier Ltd. All rights reserved.
We propose a cost-effective system for the determination of metal ion concentration in water, addressing a central issue in water resources management. The system combines novel luminometric label array technology with a machine learning algorithm that selects a minimal number of array reagents (modulators) and liquid sample dilutions, such that enable accurate quantification. The algorithm is able to identify the optimal modulators and sample dilutions leading to cost reductions since less manual labour and resources are needed. Inferring the ion detector involves a unique type of a structured feature selection problem, which we formalize in this paper. We propose a novel Cartesian greedy forward feature selection algorithm for solving the problem. The novel algorithm was evaluated in the concentration assessment of five metal ions and the performance was compared to two known feature selection approaches. The results demonstrate that the proposed system can assist in lowering the costs with minimal loss in accuracy. (c) 2017 Elsevier Ltd. All rights reserved.
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