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Exceptional Model Mining meets Multi-Objective Optimization: Application to Plant Growth Recipes in Controlled Environments

Alexandre Millot 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In today's society, information is becoming ever more pervasive. With the advent of the digital age, collecting and storing these near-infinite quantities of data is becoming increasingly easier. In this context, designing new Pattern Discovery methods, that allow for the semi-automatic discovery of relevant information and knowledge, is crucial. We consider data made of a set of descriptive attributes, where one or several of these attributes can be considered as target label(s). When a unique target label is considered, the Subgroup Discovery task aims at discovering subsets of objects -- subgroups -- whose target label distribution significantly deviates from that of the overall data. Exceptional Model Mining is a generalization of Subgroup Discovery. It is a recent framework that enables the discovery of significant local deviations in complex interactions between several target labels. In a world where everything has to be optimized, Multi-objective Optimization methods, which find the optimal trade-offs between numerous competing objectives, are of the essence. Although these research fields have given an extensive literature, their cross-fertilization has been considered only sparsely. Given collected data about a process of interest, we investigate the design of methods for the discovery of relevant parameter values driving the its optimization. Our first contribution is OSMIND, a Subgroup Discovery algorithm that returns an optimal pattern in purely numerical data. OSMIND leverages advanced techniques for search space reduction that guarantee the optimality of the discovery. Our second contribution consists of a generic iterative framework that leverages the actionability of Subgroup Discovery to solve optimization problems. Our third and main contribution is Exceptional Pareto Front Mining, a new class of models for Exceptional Model Mining that involves cross-fertilization between Pattern Discovery and Multi-objective Optimization. In-depth empirical studies have been carried out on each contribution to illustrate their relevance. Our methods are generic and can be applied to many application domains. To assess the actionability of our contributions in real life, we consider the problem of plant growth recipe optimization in controlled environments such as urban farms, the application scenario that has motivated our work. It is an intrinsic Multi-objective Optimization problem. We want to apply our pattern discovery methods to discover parameter values that lead to an optimized growth. Indeed, finding optimal settings could have tremendous repercussions on the profitability of urban farms. On synthetic and real-life data, we show that our methods allow for the discovery of parameter values that optimize the yield-cost trade-off of growth recipes.
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Submitted on : Thursday, October 21, 2021 - 10:59:48 AM
Last modification on : Tuesday, October 26, 2021 - 4:00:24 AM
Long-term archiving on: : Saturday, January 22, 2022 - 6:39:08 PM


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  • HAL Id : tel-03390102, version 1


Alexandre Millot. Exceptional Model Mining meets Multi-Objective Optimization: Application to Plant Growth Recipes in Controlled Environments. Computer Science [cs]. INSA Lyon, 2021. English. ⟨tel-03390102⟩



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