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In machine learning applications, there are scenarios of having no labeled training data, due to the data being rare or too expensive to obtain. In these cases, it is desirable to use readily available labeled data, that is similar to, but not the same as, the domain application of interest. Transfer learning algorithms are used to build high-performance classifiers, when the training data has different...
A transfer learning environment is characterized by a machine learning algorithm being trained with data from one domain (the source domain) and being tested on data from a different domain (the target domain). In a transfer learning scenario, the class probability of the source domain may be different from the class probability of the target domain, which is referred to as "domain class imbalance"...
Previous research focusing on the evaluation of transfer learning algorithms has predominantly used real-world datasets to measure an algorithm's performance. A test with a real-world dataset exposes an algorithm to a single instance of distribution difference between the training (source) and test (target) datasets. These previous works have not measured performance over a wide-range of source and...
Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for...
Understanding the sentiment conveyed by a person is a crucial task in any social interaction. Moreover, it can be used to gain insight and understanding of views held by many people. Sentiment classification is not limited to human interaction, as text can also convey the sentiment of the author. Opinion mining in text is a long studied field in machine learning. This study focuses on two of the many...
Sentiment classification of tweets is used for a variety of social sensing tasks and provides a means of discerning public opinion on a wide range of topics. A potential concern when performing sentiment classification is that the training data may contain class imbalance, which can negatively affect classification performance. A classifier trained on imbalanced data may be biased in favor of the...
Boosting methods have been successfully applied in a wide variety of machine learning applications. In the context of data quality issues, a number of variants of the standard boosting method have been proposed and evaluated. To address the problem of mislabeled examples, ORBoost was developed to prevent over fitting to noisy examples. Our research group has recently proposed RUSBoost as an enhancement...
Noise filtering is a commonly-used methodology to improve the performance of learners built using low-quality data. A common type of noise filtering is a data preprocessing technique called classification filtering. In classification filtering, a classifier is built and evaluated on the training dataset (typically using cross-validation) and any misclassified instances are considered noisy. The strategies...
Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers...
Three important data characteristics that can substantially impact a data mining project are class imbalance, poor data quality and the size of the training dataset. Data sampling is a commonly used method for improving learner performance when data is imbalanced. However, little effort has been put forth to investigate the performance of data sampling techniques when data is both noisy and imbalanced...
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural...
Decision tree learning in the presence of imbalanced data is an issue of great practical importance, as such data is ubiquitous in a wide variety of application domains. We propose hybrid data sampling, which uses a combination of two sampling techniques such as random oversampling and random undersampling, to create a balanced dataset for use in the construction of decision tree classification models...
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