The correct use of model evaluation, model selection, and algorithm selection techniques is
vital in academic machine learning research as well as in many industrial settings. This …

Recently, feature selection and dimensionality reduction have become fundamental tools for
many data mining tasks, especially for processing high-dimensional data such as gene …

The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …

Background & Aims Cancer stem cells (CSCs) can contribute to hepatocellular carcinoma
(HCC) progression and recurrence after therapy. The presence of tumor-associated …

Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …

Selected patients with hepatocellular carcinoma are candidates to receive potentially
curative treatments, such as hepatic resection or liver transplantation, but nevertheless there …

These recommendations provide a data-supported approach to the diagnosis, staging and
treatment of patients diagnosed with hepatocellular carcinoma (HCC). They are based on …

Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, and prior attempts to
develop genomic-based classification for HCC have yielded highly divergent results …

Background Cross-validation (CV) is an effective method for estimating the prediction error
of a classifier. Some recent articles have proposed methods for optimizing classifiers by …

Machine learning is a branch of artificial intelligence that employs a variety of statistical,
probabilistic and optimization techniques that allows computers to “learn” from past …