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 ...

Handling non-linear constraints in continuous optimization is challenging, and finding a feasible solution is usually a difficult task. In the past few decades, various techniques have been developed to deal with linear ...

Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this ...

The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ- ent strategies can be employed to efficiently generate adversarial attacks, some ...

The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision or sensor networks, represents a challenge for the K-means algorithm. In this regard, ...

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 ...

In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for ...

Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails ...

Adversarial examples are inputs subtly perturbed to produce a wrong prediction in machine learning models, while remaining perceptually similar to the original input. To find adversarial examples, some attack strategies ...

An End-Of-Turn Detection Module (EOTD-M) is an essential component of au- tomatic Spoken Dialogue Systems. The capability of correctly detecting whether a user’s utterance has ended or not improves the accuracy in interpreting ...