Out-of-hospital emergency departments receive multiple types of requests daily. Their management requires a balance to be found between available resources and the actual needs of the requesting party. Those regarding suicidal behaviour, which are resource heavy, are few in number in terms of the bulk of requests, and detecting them correctly is therefore important. Previous research, using machine learning algorithms to analyse suicide, has typically focused on discovering insights to be used by medical personnel. This proposal extends its use in two directions: knowledge that can be used by non-exclusively medical staff, such as telephone operators, and the models that have been incorporated into a software prototype to help in the decision-making of an emergency department. In addition, previous research has often included a range of information from different sources that are not available when processing an emergency call request, for example, data that is only obtained at the end of the intervention. A full-scale data mining process has been performed using data from the out-of-hospital emergency service in Malaga (Spain). Sensitivity has been the primary goal to avoid missing cases requiring special attention, but this objective has been pursued without overlooking a good trade-off with specificity. The best models can offer such a compromise between sensitivity and specificity, and show more than 80% in both metrics simultaneously. The experts validate that the modelling phase showed that the algorithms have automatically identified already known situations. This lays the groundwork for further iterations with a promising outlook.

A new methodology based on expert knowledge and data mining is proposed to obtain data-driven models that characterize household consumption profiles. These profiles are useful for electricity marketers to understand their customers’ consumption. They could then adjust their electricity purchases in the market and provide recommendations to their customers to manage their consumption. The novelty of this research work is proposing a new procedure to determine an adequate number of clusters for a clustering task. Therefore, the proposed new methodology includes this novel procedure to build the models in two phases. In the first phase, clustering algorithms are used to group the data using different numbers of clusters. For the second phase, a new procedure (k-ISAC_TLP) is proposed and used to select the most appropriate number of clusters. This methodology allows the inclusion of domain information. In the case of household electricity consumption, where only groups with a significant number are relevant as long as the error is small, it allows the use of metrics like the mean absolute error and the number of observations (daily electricity consumption profiles). According to experts, the results achieved in two real datasets (from Spain and Ireland), with millions of observations support the methodology and reveal novel knowledge. In both cases, two and a half million observations have been analyzed and around twenty electricity consumption profiles have been detected. The methodology is easily extensible to problems of any domain where clustering algorithms are applicable. A software solution has been implemented and made freely available.

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.

Classification trees are a powerful tool for mining non-stationary data streams. In these situations, mas sive data are constantly generated at high speed and the underlying target function can change over time. The iadem family of algorithms is based on Hoeffding’s and Chernoff’s bounds and induces online deci sion trees from data streams, but is not able to handle concept drift. This study extends this family to deal with time-changing data streams. The new online algorithm, named iadem-3, performs two main actions in response to a concept drift. Firstly, it resets the variables affected by the change and main tains unbroken the structure of the tree, which allows for changes in which consecutive target functions are very similar. Secondly, it creates alternative models that replace parts of the main tree when they significantly improve the accuracy of the model, thereby rebuilding the main tree if needed. An online change detector and a non-parametric statistical test based on Hoeffding’s bounds are used to guaran tee this significance. A new pruning method is also incorporated in iadem-3, making sure that all split tests previously installed in decision nodes are useful. The learning model is also viewed as an ensem ble of classifiers, and predictions of the main and alternative models are combined to classify unlabeled examples. iadem-3 is empirically compared with various well-known decision tree induction algorithms for concept drift detection. We empirically show that our new algorithm often reaches higher levels of accuracy with smaller decision tree models, maintaining the processing time bounded, irrespective of the number of instances processed.

Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change overtime, an inherent problem of online learning (known as concept drift). In order to handle concept drift regardless of the learning model, we propose new methods to monitor the performance metrics measured during the learning process, to trigger drift signals when a significant variation has been detected. To monitor this performance, we apply some probability inequalities that assume only independent, univariate and bounded random variables to obtain theoretical guarantees for the detection of such distributional changes. Some common restrictions for the online change detection as well as relevant types of change (abrupt and gradual) are considered. Two main approaches are proposed, the first one involves moving averages and is more suitable to detect abrupt changes. The second one follows a widespread intuitive idea to deal with gradual changes using weighted moving averages. The simplicity of the proposed methods, together with the computational efficiency make them very advantageous. We use a Naive Bayes classifier and a Perceptron to evaluate the performance of the methods over synthetic and real data.

Multi-label learning is a very interesting field in Machine Learning. It allows to generalise standard methods and evaluation procedures, and tackle challenging real problems where one example can be tagged with more than one label. In this paper we study the performance of different multi-label methods in combination with standard single-label algorithms, using several specific multi-label metrics. What we want to show is how a good preprocessing phase can improve the performance of such methods and algorithms. As we will explain, its main advantage is a shorter time to induce the models, while keeping (even improving) other classification quality measures. We use the GNUsmail framework to do the preprocessing of an existing and extensively used dataset, to obtain a reduced feature space that conserves the relevant information and allows improvements on performance. Thanks to the capabilities of GNUsmail, the preprocessing step can be easily applied to different email datasets.

Real-time classification of massive email data is a challenging task that presents its own particular difficulties. Since email data presents an important temporal component, several problems arise: emails arrive continuously, and the criteria used to classify those emails can change, so the learning algorithms have to be able to deal with concept drift. Our problem is more general than spam detection, which has received much more attention in the literature.

Two extensive research areas in Machine Learning are classification and prediction. Many approaches have been focused in the induction of ensemble to increase learning accuracy of individual classifiers. Recently, new approaches, different to those that look for accurate and diverse base classifiers, are emerging. In this paper we present a system made up of two layers: in the first layer, one ensemble classifier process every example and tries to classify them; in the second layer, one individual classifier is induced using the examples that are not unanimously classified by the ensemble. In addition, the examples that reach to the second layer incorporate new information added in the ensemble. Thus, we can achieve some improvement in the accuracy level, because the second layer can do more informed classifications. In the experimental section we present some results that suggest that our proposal can actually improve the accuracy of the system.