Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging

AAAI Conferences

Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in any supervised approach such as co-training or selflearning. We empirically show that classification performance increases by improving the semi-supervised algorithm's ability to correctly assign labels to previouslyunlabelled data.


The Hitchhiker's Guide to Machine Learning in Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


The Hitchhiker's Guide to Machine Learning in Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


Machine learning: Supervised methods (PDF Download Available)

#artificialintelligence

We'll illustrate SVM using a two-class problem and begin with Typically, C is chosen using cross-validation2. Points at the margin's edge (black outlines) are called The margin is now 0.64 with six support vectors. AU: the title is long and a bit clunky. What do you think about deleting'supervised methods' from it?


Semi-Supervised Learning for Imbalanced Sentiment Classification

AAAI Conferences

Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, most existing studies assume the balance between negative and positive samples in both the labeled and unlabeled data, which may not be true in reality. In this paper, we investigate a more common case of semi-supervised learning for imbalanced sentiment classification. In particular, various random subspaces are dynamically generated to deal with the imbalanced class distribution problem. Evaluation across four domains shows the effectiveness of our approach.