Constellation, le dépôt institutionnel de l'Université du Québec à Chicoutimi

Résumé

Classifications may be defined as the result of the process by which similar objects are recognized and categorized through the separation of elements of a system into groups of response (Everitt et al., 2011). This is done by submitting variables to a classifier, that first quantifies the similarity between samples according to a set of criteria and then regroups (or classifies) samples in order to maximize within-group similarity and minimize between-group similarity (Everitt et al., 2011).

Classifications have become critical in many disciplines. In spatial ecology, for example, grouping locations with similar features may help the detection of areas driven by the same ecological processes and occupied by same species (Fortin and Dale, 2005; Elith et al., 2006), which can support conservation actions. In fact, classifications have been used with the aim of investigating the spatial distribution of target categories such as habitats (Coggan and Diesing, 2011), ecoregions (Fendereski et al., 2014), sediment classes (Hass et al., 2017), or biotopes (Schiele et al., 2015). Sometimes such classifications were found to act as surrogates for biodiversity in data-poor regions (e.g., Lucieer and Lucieer, 2009; Huang et al., 2012), some class being known for supporting higher biodiversity. Many of the traditional classification methods were developed in order to reduce system complexity (Fortin and Dale, 2005) by imposing discrete boundaries between elements of a system; it is easier for the human mind to simplify complex systems by identifying discrete patterns (Eysenck and Keane, 2010), and grouping similar elements together (Everitt et al., 2011). However, in natural environments, spatial and temporal transitions between elements of a system are often gradual (e.g., an intertidal flat transitioning from land to sea) (Farina, 2010). Those transitions may display distinct properties from those of the two elements they separate. Despite the particularities and importance of such transitions, they are often disregarded in ecological research (Foody, 2002), leading to the adoption of approaches that, by defining sharp boundaries, may fail to appropriately describe natural patterns and groups of a system. Such approaches have become the norm, despite the existence of approaches such as, fuzzy logic (Zadeh, 1965) and machine learning (Kuhn and Johnson, 2013) that are able to offer a more representative description of those natural transitional zones. In ecology for instance, machine learning approaces have gained some traction because of their ability to predict classes distribution (area-wide) in data-poor conditions (e.g., sparse punctual information) with a relative high performance and with no particular assumption in building the relationship between targetted classes and physical parameters (e.g., Barry and Elith, 2006; Brown et al., 2011; Fernández-Delgado et al., 2014).

In the present contribution, we aim at highlighting the limitations associated with classification techniques that are based on Boolean logic (i.e., true/false) and that impose discrete boundaries to systems. We propose to shift practices toward techniques that learn from the system under study by adopting soft classification to support uncertainty evaluation.