This paper presents a novel machine learning approach to per- form an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: i) in ...

Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...

A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series ...

The Quadratic Assignment Problem (QAP) is a well-known permutation-based combinatorial optimization problem with real applications in industrial and logistics environments. Motivated by the challenge that this NP-hard ...

A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the ...

Smoothing techniques have become one of the most popular modelling approaches in the unidimensional and multidimensional setting. However, out-of-sample prediction in the context of smoothing models is still an open problem ...

According to ecological niche theory, species response curves are unimodal with respect to environmental gradients. A variety of statistical methods have been developed for species distribution modelling. A general problem ...

This paper deals with supervised classification of multivariate time se- ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown ...

The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs ...

This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection or false alarm ...