Browsing by Keyword "Decision trees"
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Item A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities(2020-12) González, Sergio; García, Salvador; Del Ser, Javier; Rokach, Lior; Herrera, Francisco; IAEnsembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily. Therefore, it is necessary to identify which are the appropriate algorithms for a certain problem. In this paper, we aim to help practitioners to choose the best ensemble technique according to their problem characteristics and their workflow. To do so, we revise the most renowned bagging and boosting algorithms and their software tools. These ensembles are described in detail within their variants and improvements available in the literature. Their online-available software tools are reviewed attending to the implemented versions and features. They are categorized according to their supported programming languages and computing paradigms. The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency. This comparison is done under the same software environment with 76 different classification tasks. Their predictive capabilities are evaluated with a wide variety of scenarios, such as standard multi-class problems, scenarios with categorical features and big size data. The efficiency of these methods is analyzed with considerably large data-sets. Several practical perspectives and opportunities are also exposed for ensemble learning.Item Vulnerability assessment of cultural heritage sites towards flooding events(2018-06-18) Gandini, Alessandra; Egusquiza, Aitziber; Garmendia, Leire; San-José, José-Tomás; Tecnalia Research & Innovation; LABORATORIO DE TRANSFORMACIÓN URBANAHistoric sites are threatened by diverse weather patterns, mainly due to global climate change, such as sea-level rise and increasing frequency of storms and other extreme precipitation events. As climate change is becoming an increasing urban problem, heritage located in this context is considered as a sensitive and vulnerable element of the city. Adaptation should be oriented to a sustainable transformation of the historic city, leading to more resilient and safe environments. Risk-based approaches should incorporate an assessment of sensitiveness and capacity to adapt to these hazards. Vulnerability is often assessed on a large scale (e.g. regional, local) and buildings are not considered as part of the urban environment, while conservation is often developed on the operational scale of a monument or site. Management of cultural heritage requires therefore for an urban approach, which considers all the elements and buildings as part of the urban environment. Research presented in this paper describes a methodological approach (MIVES - Integrated Value Model for Sustainability Assessment) for vulnerability assessment of historic sites, supported by multilevel indicators (urban, building, element), in order to provide an informed decision-making. The solution proposed in based on an organised and structures decision tree, which provides a comparable and unique vulnerability index on the building level.