Abstract

Abstract
We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually.

Abstract

Abstract
The main purpose of this work was to design, develop, and construct a simple desktop AC susceptometer to monitor in situ and in real time the coprecipitation synthesis of magnetic nanoparticles. The design incorporates one pair of identical pick-up sensing coils and one pair of Helmholtz coils. The picked up signal is detected by a lock-in SR850 amplifier that measures the in-and out-of-phase signals. The apparatus also includes a stirrer with 45 degrees-angle blades to promote the fast homogenization of the reaction mixture. Our susceptometer has been successfully used to monitor the coprecipitation reaction for the synthesis of iron oxide nanoparticles. (C) 2015 AIP Publishing LLC.

Abstract
This paper describes a new type of ensembles that aims at improving the predictive performance of these approaches in time series forecasting. Ensembles are recognised as one of the most successful approaches to prediction tasks. Previous theoretical studies of ensembles have shown that one of the key reasons for this performance is diversity among ensemble members. Several methods exist to generate diversity. The key idea of the work we are presenting here is to propose a new form of diversity generation that explores some specific properties of time series prediction tasks. Our hypothesis is that the resulting ensemble members will be better at addressing different dynamic regimes of time series data. Our large set of experiments confirms that the methods we have explored for generating diversity are able to improve the performance of the equivalent ensembles with standard diversity generation procedures. © 2014 M. Oliveira & L. Torgo.