Multiobjective evolutionary optimization of quadratic Takagi-Sugeno fuzzy rules for remote bathymetry estimation

In this work we tackle the problem of bathymetry estimation using: i) a multispectral optical image of the region of interest, and ii) a set of in situ measurements. The idea is to learn the relation that between the reflectances and the depth using a supervised learning approach. In particular, quadratic Takagi-Sugeno fuzzy rules are used to model this relation. The rule base is optimized by means of a multiobjective evolutionary algorithm. To the best of our knowledge this work represents the first use of a quadratic Takagi-Sugeno fuzzy system optimized by a multiobjective evolutionary algorithm with bounded complexity, i.e., able to control the complexity of the consequent part of second-order fuzzy rules. This model has an outstanding modeling power, without inheriting the drawback of complexity due to the use of quadratic functions (which have complexity that scales quadratically with the number of inputs). This opens the way to the use of the proposed approach even for medium/high dimensional problems, like in the case of hyper-spectral images

Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004)

The invasion of freshwater ecosystems is a particularly alarming phenomenon in the Iberian Peninsula. Habitat suitability modelling is a proficient approach to extract knowledge about species ecology and to guide adequate management actions. Decision-trees are an interpretable modelling technique widely used in ecology, able to handle strongly nonlinear relationships with high order interactions and diverse variable types. Decision-trees recursively split the input space into two parts maximising child node homogeneity. This recursive partitioning is typically performed with axis-parallel splits in a top-down fashion. However, recent developments of the R packages oblique.tree, which allows the development of oblique split-based decision-trees, and evtree, which performs globally optimal searches with evolutionary algorithms to do so, seem to outperform the standard axis-parallel top-down algorithms; CART and C5.0. To evaluate their possible use in ecology, the two new partitioning algorithms were compared with the two well-known, standard axis-parallel algorithms. The entire process was performed in R by simultaneously tuning the decision-tree parameters and the variables subset with a genetic algorithm and modelling the presence-absence of the Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004), an invasive fish species that has spread across the Iberian Peninsula. The accuracy and complexity of the trees, the modelled patterns of mesohabitat selection and the variables importance were compared. None of the new R packages, namely oblique.tree and evtree, outperformed the C5.0 algorithm. They rendered almost the same decision-trees as the CART algorithm, although they were completely interpretable they performed from four to eight partitions in comparison with C5.0, which resulted in a more complex structure with 17 partitions. Oblique.tree proved to be affected by prevalence and it does not include the possibility of weighting the observations, which potentially discourage its actual use. Although the use of evtree did not suggest a major improvement compared with the remaining packages, it allowed the development of regression trees which may be informative for additional modelling tasks such as abundance estimation. Looking at the resulting decision-trees, the optimal habitats for the Iberian gudgeon were large pools in lowland river segments with depositional areas and aquatic vegetation present, which typically appeared in the form of scattered macrophytes clumps. Furthermore, Iberian gudgeon seem to avoid habitats characterised by scouring phenomena and limited vegetated cover availability. Accordingly, we can assume that river regulation and artificial impoundment would have favoured the spread of the Iberian gudgeon across the entire peninsula.The study has been partially funded by the national Research project IMPADAPT (CGL2013-48424-C2-1-R) with MINECO (Spanish Ministry of Economy) and Feder funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). This study was also supported in part by the University Research Administration Center of the Tokyo University of Agriculture and Technology. Finally, we are grateful to the colleagues who worked in the field data collection, especially Juan Diego Alcaraz-Henandez, Rui M. S. Costa and Aina Hernandez.Muñoz Mas, R.; Fukuda, S.; Vezza, P.; Martinez-Capel, F. (2016). Comparing four methods for decision-tree induction: a case study on the invasive Iberian gudgeon (Gobio lozanoi; Doadrio & Madeira, 2004). Ecological Informatics. 34:22-34. https://doi.org/10.1016/j.ecoinf.2016.04.011S22343

Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938)

Human activities have altered flow regimes resulting in increased pressures and threats on river biota. Physical habitat simulation has been established as a standard approach among the methods for Environmental Flow Assessment (EFA). Traditionally, in EFA, univariate habitat suitability curves have been used to evaluate the habitat suitability at the microhabitat scale whereas Generalized Additive Models (GAMs) and fuzzy logic are considered the most common multivariate approaches to do so. The assessment of the habitat suitability for three size classes of the West Balkan trout (Salmo farioides; Karaman, 1938) inferred with these multivariate approaches was compared at three different levels. First the modelled patterns of habitat selection were compared by developing partial dependence plots. Then, the habitat assessment was spatially explicitly compared by calculating the fuzzy kappa statistic and finally, the habitat quantity and quality was compared broadly and at relevant flows under a hypothetical flow regulation, based on the Weighted Usable Area (WUA) vs. flow curves. The GAMs were slightly more accurate and the WUA-flow curves demonstrated that they were more optimistic in the habitat assessment with larger areas assessed with low to intermediate suitability (0.2 0.6). Nevertheless, both approaches coincided in the habitat assessment (the optimal areas were spatially coincident) and in the modelled patterns of habitat selection; large trout selected microhabitats with low flow velocity, large depth, coarse substrate and abundant cover. Medium sized trout selected microhabitats with low flow velocity, middle-to-large depth, any kind of substrate but bedrock and some elements of cover. Finally small trout selected microhabitats with low flow velocity, small depth, and light cover only avoiding bedrock substrate. Furthermore, both approaches also rendered similar WUA-flow curves and coincided in the predicted increases and decreases of the WUA under the hypothetical flow regulation. Although on an equal footing, GAMs performed slightly better, they do not automatically account for variables interactions. Conversely, fuzzy models do so and can be easily modified by experts to include new insights or to cover a wider range of environmental conditions. Therefore, as a consequence of the agreement between both approaches, we would advocate for combinations of GAMs and fuzzy models in fish-based EFA.This study was supported by the ECOFLOW project funded by the Hellenic General Secretariat of Research and Technology in the framework of the NSRF 2007-2013. We are grateful for field assistance of Dimitris Kommatas, Orfeas Triantafillou and Martin Palt and to Alcibiades N. Economou for assistance in discussions on trout biology and ecology.Muñoz Mas, R.; Papadaki, C.; Martinez-Capel, F.; Zogaris, S.; Ntoanidis, L.; Dimitriou, E. (2016). Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938). Ecological Engineering. 91:365-377. doi:10.1016/j.ecoleng.2016.03.009S3653779

Weight-Selected Attribute Bagging for Credit Scoring

Assessment of credit risk is of great importance in financial risk management. In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk. Weights of attributes are first computed using attribute evaluation method such as linear support vector machine (LSVM) and principal component analysis (PCA). Subsets of attributes are then constructed according to weights of attributes. For each of attribute subsets, the larger the weights of the attributes the larger the probabilities by which they are selected into the attribute subset. Next, training samples and test samples are projected onto each attribute subset, respectively. A scoring model is then constructed based on each set of newly produced training samples. Finally, all scoring models are used to vote for test instances. An individual model that only uses selected attributes will be more accurate because of elimination of some of redundant and uninformative attributes. Besides, the way of selecting attributes by probability can also guarantee the diversity of scoring models. Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods

Learning lost temporal fuzzy association rules

Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.EPSRC DT

Fuzzy Sets, Fuzzy Logic and Their Applications 2020

The present book contains the 24 total articles accepted and published in the Special Issue “Fuzzy Sets, Fuzzy Logic and Their Applications, 2020” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of fuzzy sets and systems of fuzzy logic and their extensions/generalizations. These topics include, among others, elements from fuzzy graphs; fuzzy numbers; fuzzy equations; fuzzy linear spaces; intuitionistic fuzzy sets; soft sets; type-2 fuzzy sets, bipolar fuzzy sets, plithogenic sets, fuzzy decision making, fuzzy governance, fuzzy models in mathematics of finance, a philosophical treatise on the connection of the scientific reasoning with fuzzy logic, etc. It is hoped that the book will be interesting and useful for those working in the area of fuzzy sets, fuzzy systems and fuzzy logic, as well as for those with the proper mathematical background and willing to become familiar with recent advances in fuzzy mathematics, which has become prevalent in almost all sectors of the human life and activity

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management