## Search

Now showing items 1-10 of 11

#### A Machine Learning Approach to Predict Healthcare Cost of Breast Cancer Patients

(2021)

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 ...

#### Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.

(2020-12-29)

The aim of this paper is to analyze the sequence of actions in the health system associated with a particular disease. In order to do that, using Electronic Health Records, we define a general methodology that allows us ...

#### An efficient K-means clustering algorithm for tall data

(2020)

The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial ...

#### On-line Elastic Similarity Measures for time series

(2019-04)

The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are ...

#### Are the artificially generated instances uniform in terms of difficulty?

(2018-06)

In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ...

#### On-Line Dynamic Time Warping for Streaming Time Series

(2017-09)

Dynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning ...

#### Nature-inspired approaches for distance metric learning in multivariate time series classification

(2017-07)

The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular ...

#### An efficient approximation to the K-means clustering for Massive Data

(2017-02-01)

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...

#### Efficient approximation of probability distributions with k-order decomposable models

(2016-07)

During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models. Some of these algorithms can be used to search for a maximum likelihood decomposable ...

#### An efficient approximation to the K-means clustering for Massive Data

(2016-06-28)

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial ...