Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques together with the market scarcity on ML experts, has created the need for systems t...

Boosting and Bagging, as two representative approaches to learning classiier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build diierent classiiers using a base learning algorithm by changing the distribution of the training set. Sasc, as a diierent type of committee learning method, can also signiicantly reduce the error rate of decision t...

In recent years, semi-supervised learning has been a hot research topic in machine learning area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each example is represented by two or more r...

In this paper, we report on our experiments on the Yahoo! Labs Learning to Rank challenge organized in the context of the 23rd International Conference of Machine Learning (ICML 2010). We competed in both the learning to rank and the transfer learning tracks of the challenge with several tree-based ensemble methods, including Tree Bagging (Breiman, 1996), Random Forests (Breiman, 2001), and Ext...

In the paper the comparison of ensemble based methods applied to censored survival data was conducted. Bagging survival trees, dipolar survival tree ensemble and random forest were taken into consideration. The prediction ability was evaluated by the integrated Brier score, the prediction measure developed for survival data. Two real datasets with different percentage of censored observations w...

In the wake of recent pandemic COVID-19, we explore its unprecedented impact on demand and supply cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory Noise (NBG). The study employed filters to enha...

Ensemble learning for improving weak classifiers is one important direction in the current research of machine learning, and thereinto bagging, boosting and random subspace are three powerful and popular representatives. They have so far shown efficacies in many practical classification problems. However, for electroencephalogram (EEG) signal classification with application to brain–computer in...

BACKGROUND The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. RESULTS The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. A...