Contributions to Time Series Classification: Meta-Learning and Explainability

This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In this context, the number of methods available for classifying time series is increasing, and the classifiers are becoming more and more competitive and varied. Thus, the first contribution of the thesis consists of proposing a taxonomy of distance-based time series classifiers, where an exhaustive review of the existing methods and their computational costs is made. Moreover, from the point of view of a non-expert user (even from that of an expert), choosing a suitable classifier for a given problem is a difficult task. The second contribution, therefore, deals with the recommendation of time series classifiers, for which we will use a meta-learning approach. Finally, the third contribution consists of proposing a method to explain the prediction of time series classifiers, in which we calculate the relevance of each region of a series in the prediction. This method of explanation is based on perturbations, for which we will consider specific and realistic transformations for the time series.BES-2016-07689

Ad-Hoc Explanation for Time Series Classification

In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and specific for time series. In real-world time series, variations in the speed or the scale of a particular action, for instance, may determine the class, so modifying this type of characteristic leads to ad-hoc explanations for time series. To this end, four perturbations or transformations are proposed: warp, scale, noise, and slice. Given a transformation, an interval of a series is considered relevant for the prediction of a classifier if a transformation in this interval changes the prediction. Another novelty is that the method provides a two-level explanation: a high-level explanation, where the robustness of the prediction with respect to a particular transformation is measured, and a low-level explanation, where the relevance of each region of the time series in the prediction is visualized. In order to analyze and validate our proposal, first some illustrative examples are provided, and then a thorough quantitative evaluation is carried out using a specifically designed evaluation procedure.PID2019-104966GB-I00 3KIA-KK2020/004

A review on distance based time series classification

Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to its simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.TIN2016-78365-

Time Series Classifier Recommendation by a Meta-Learning Approach

This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set and best classifier. For this, an ad-hoc set of quick estimators of the accuracies of the candidate classifiers (landmarkers) are designed, which are used as predictors for the recommendation system. The performance of our recommender is compared with the performance of a standard method for non-sequential data and a set of baseline methods, which our method outperforms in 7 of the 9 considered scenarios. Since some meta-targets can be inferred from the predictions of other more fine-grained meta-targets, the last part of the work addresses the hierarchical inference of meta-targets. The experimentation suggests that, in many cases, a single model is sufficient to output many types of meta-targets with competitive results

Prevalence of Buruli Ulcer in Akonolinga Health District, Cameroon: Results of a Cross Sectional Survey

As long as there is no strategy to prevent Buruli ulcer, the early detection and treatment of cases remains the most promising control strategy. Buruli ulcer is most common in remote rural areas where people have little contact with health structures. Information on the number of existing cases in the population and where they go to seek treatment is important for project planning and evaluation. Health structure based surveillance systems cannot provide this information, and previous prevalence surveys did not provide information on spatial distribution and coverage. We did a survey using centric systematic area sampling in a Health District in Cameroon to estimate prevalence and project coverage. We found the method was easy to use and very useful for project planning. It identified priority areas with relatively high prevalence and low coverage and provided an estimate of the number of existing cases in the population of the health district. The active case finding component of the method used served as an awareness campaign and was an integrated part of the project, creating a network of health delegates trained on Buruli ulcer

Structural sustainability appraisal in BIM

The provision of Application Programming Interface (API) in BIM-enable tools can contribute to facilitating BIM-related research. APIs are useful links for running plug-ins and external programmes but they are yet to be fully exploited in expanding the BIM scope. The modelling of n-Dimensional (nD) building performance measures can potentially benefit from BIM extension through API implementations. Sustainability is one such measure associated with buildings. For the structural engineer, recent design criteria have put great emphasis on the sustainability credentials as part of the traditional criteria of structural integrity, constructability and cost. This paper examines the utilization of API in BIM extension and presents a demonstration of an API application to embed sustainability issues into the appraisal process of structural conceptual design options in BIM. It concludes that API implementations are useful in expanding the BIM scope. Also, the approach including process modelling, algorithms and object-based instantiations demonstrated in the API implementation can be applicable to other nD building performance measures as may be relevant to the various professional platforms in the construction domain

Antioxidant pathways are up-regulated during biological nitrogen fixation to prevent ROS-induced nitrogenase inhibition in Gluconacetobacter diazotrophicus

Gluconacetobacter diazotrophicus, an endophyte isolated from sugarcane, is a strict aerobe that fixates N2. This process is catalyzed by nitrogenase and requires copious amounts of ATP. Nitrogenase activity is extremely sensitive to inhibition by oxygen and reactive oxygen species (ROS). However, the elevated oxidative metabolic rates required to sustain biological nitrogen fixation (BNF) may favor an increased production of ROS. Here, we explored this paradox and observed that ROS levels are, in fact, decreased in nitrogen-fixing cells due to the up-regulation of transcript levels of six ROS-detoxifying genes. A cluster analyses based on common expression patterns revealed the existence of a stable cluster with 99.8% similarity made up of the genes encoding the α-subunit of nitrogenase Mo–Fe protein (nifD), superoxide dismutase (sodA) and catalase type E (katE). Finally, nitrogenase activity was inhibited in a dose-dependent manner by paraquat, a redox cycler that increases cellular ROS levels. Our data revealed that ROS can strongly inhibit nitrogenase activity, and G. diazotrophicus alters its redox metabolism during BNF by increasing antioxidant transcript levels resulting in a lower ROS generation. We suggest that careful controlled ROS production during this critical phase is an adaptive mechanism to allow nitrogen fixation

Outlining a new collaborative business model as a result of the green Building Information Modelling impact in the AEC supply chain

BIM (Building Information Modelling) technological push has enabled to integrate the design/construction outcomes of 3D-CAD along the product/service AEC (Architecture, Engineering and Construction) SC (supply chain) through an intelligent DMS (Data Management System) based on standard and interoperable data formats. The proposed end-to-end approach overcomes a typical AEC gap, enables the operationalisation of the sustainable/green building LCA (Life Cycle Assessment) and puts together new collaborative relationships with the owner, among SC stakeholders and with new forms of BIM procurement. The outlined collaborative business model is based on the Quality Control and Assurance framework and provides conceptual consistency to the reintroduction of the owner concerns/satisfaction in the SC, as well as enables consistent and accountable relationships between (smart)materials procurement and building specification. An expert’s focus group carried out a preliminary check of the model’s interest/applicability, resulting in recommendations for its further detailing and for propositions development into a systematic enquiring process.info:eu-repo/semantics/acceptedVersio