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Unsupervised learning aims to discovery latent representation embedded in the observation, which is useful for data visualization, dimensionality reduction, and density modeling. Autoencoders have been successfully used to learn the latent variations in data, especially with the recent reintroduction by deep learning. For some specific tasks, there are supervised information or labels that can be...
Nowadays, transfer learning is one of the main research areas in machine learning that is helpful for labeling the data with low cost. In this paper, we propose a novel bagging-based ensemble transfer learning (BETL). The BETL framework includes three operations: Initiate, Update, and Integrate. In the Initiate operation, we use bootstrap sampling to divide the source data into many subsets, and add...
The lack of labeled training data is a common issue in many machine learning applications. Semi-supervised learning addresses this issue by self-labeling unlabelled examples. Transfer learning tackles it from a different way: borrow labeled examples from a different but related domain (source domain) by assigning weights to those examples based on their suitability on the new domain (target domain)...
When dealing with the imbalanced datasets (IDS), the hyperplane of Support vector machine (SVM) tends to minority class (positive class), which causes low classification accuracy. Aiming at this problem, we propose a novel differential evolution-clustering hybrid resampling SVM algorithm (DEC-SVM). This algorithm utilizes the similar mutation and crossover operators of Differential Evolution (DE)...
The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced, and implicitly assume that all misclassification errors cost equally. But data in real-world is usually imbalanced. The class imbalance problem is pervasive and ubiquitous, causing trouble to a large segment of the data mining community. The tradition machine learning algorithms...
The naive Bayesian classifier provides a very simple and effective model for machine learning, but its attribute independence assumption is often violated in the real world. To improve the performance of Bayesian classifier, we present a novel algorithm called evolutional one-dependence augmented naive Bayes (EANB), which selects the attributes' parents by carrying an evolutional search through the...
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