Two-Level Text Classification Using Hybrid Machine Learning Techniques

Nowadays, documents are increasingly being associated with multi-level category hierarchies rather than a flat category scheme. To access these documents in real time, we need fast automatic methods to navigate these hierarchies. Today’s vast data repositories such as the web also contain many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each domain constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is frequently further divided into many subcategories. Subspace Learning is a technique popular with non-text domains such as image recognition to increase speed and accuracy. Subspace analysis lends itself naturally to the idea of hybrid classifiers. Each subspace can be processed by a classifier best suited to the characteristics of that particular subspace. Instead of using the complete set of full space feature dimensions, classifier performances can be boosted by using only a subset of the dimensions. This thesis presents a novel hybrid parallel architecture using separate classifiers trained on separate subspaces to improve two-level text classification. The classifier to be used on a particular input and the relevant feature subset to be extracted is determined dynamically by using a novel method based on the maximum significance value. A novel vector representation which enhances the distinction between classes within the subspace is also developed. This novel system, the Hybrid Parallel Classifier, was compared against the baselines of several single classifiers such as the Multilayer Perceptron and was found to be faster and have higher two-level classification accuracies. The improvement in performance achieved was even higher when dealing with more complex category hierarchies

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Two-level text classification using hybrid machine learning techniques

Nowadays, documents are increasingly being associated with multi-level category hierarchies rather than a flat category scheme. To access these documents in real time, we need fast automatic methods to navigate these hierarchies. Today’s vast data repositories such as the web also contain many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each domain constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is frequently further divided into many subcategories. Subspace Learning is a technique popular with non-text domains such as image recognition to increase speed and accuracy. Subspace analysis lends itself naturally to the idea of hybrid classifiers. Each subspace can be processed by a classifier best suited to the characteristics of that particular subspace. Instead of using the complete set of full space feature dimensions, classifier performances can be boosted by using only a subset of the dimensions. This thesis presents a novel hybrid parallel architecture using separate classifiers trained on separate subspaces to improve two-level text classification. The classifier to be used on a particular input and the relevant feature subset to be extracted is determined dynamically by using a novel method based on the maximum significance value. A novel vector representation which enhances the distinction between classes within the subspace is also developed. This novel system, the Hybrid Parallel Classifier, was compared against the baselines of several single classifiers such as the Multilayer Perceptron and was found to be faster and have higher two-level classification accuracies. The improvement in performance achieved was even higher when dealing with more complex category hierarchies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo