A novel one-class classification method based on feature analysis and prototype reduction

Cabral, George G.; Oliveira, Adriano L. I.

doi:10.1109/icsmc.2011.6083797

Cited by 7 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For one-class classifiers, usually Receiver Operating Characteristic(ROC) curves are used as a measurement of performance [122]. The ROC curve is created by plotting the True Positive Rate (TPR) vs the False Positive Rate (FPR).…”

Section: Covariance-guided One-class Support Vector Machine (Cosvm) Chapter 4 Novel Supervised Classifiersmentioning

confidence: 99%

Information-assisted volume rendering and visual evaluation through machine intelligence

Khan¹

2023

Preprint

View full text Add to dashboard Cite

Exploration and visualization of complex data has become an integral part of life. But there is a semantic gap between the users and the visualization scientists. The priority of the users is usability while that of the scientists is techniques. Information-Assisted Visualization (IAV) can help bridge this gap, where additional information extracted from the raw data is presented to the user in an easily interpretable way. This thesis proposes some novel machine intelligence based systems for intuitive IAV. The majority of the thesis focuses on Direct Volume Rendering, where Transfer Functions (TF) are used to color the volume data to expose structures. Existing TF design methods require manipulating complex widgets, which may be difficult for the user. We propose two novel approaches towards TF design. In the data-centric approach, we generate an organized representation of the data through clustering and provide the user with some intuitive control over the output in the cluster domain. We use Spherical Self-Organizing Maps (SS)M) as the core of this approach. Instead of manipulating complex widgets, the user interacts with the simple SSOM color-coded lattice to design the TF. In the image-centric approach, the user interaction with the data is direct and minimal. The user interactions create the training data, and supervised classification is used to generate the TF. First, we propose novel supervised classifiers that combine the local information available through Support Vector Machine-based classifiers and the global information available through Nonparametric Discriminant Analysis-based classifiers. Using these classifiers, we propose a TF design method where the user interacts with the volume slices directly to generate the output. Finally, we explore the use of IAV for home-based physical rehabilitation. We propose an information-assisted visual valuation framework which can compare a user’s performance of a physical exercise with that of an expert using our novel Incremental Dynamic Time Warping method and communicate the results visually through our color-mapped skeleton silhouette. All the proposed techniques are accompanied by detailed experimental results comparing them against the state-of-the-art. The results shows the potential of using machine learning techniques to achieve visualization tasks in a simpler yet more effective way.

show abstract

Section: Covariance-guided One-class Support Vector Machine (Cosvm) Chapter 4 Novel Supervised Classifiersmentioning

confidence: 99%

Information-assisted volume rendering and visual evaluation through machine intelligence

Khan¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Angiulli [2] introduced a Prototype-based Domain Description rule that is similar to standard nearest neighbor-based one-class classifier but exploits only a selected subset of the training set. Cabral and de Oliviera [6] proposed to analyze every limit of all the feature dimensions to find the true border which describes the normal class. Their method simulates the novelty class by creating artificial prototypes outside the normal description and then uses minimal-distance classification.…”

Section: The Role Of Instance Reduction In One-class Classificationmentioning

confidence: 99%

Instance reduction for one-class classification

et al. 2018

View full text Add to dashboard Cite

Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In

show abstract

One-Class Classification through Optimized Feature Boundaries Detection and Prototype Reduction

Cabral

Oliveira

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A novel one-class classification method based on feature analysis and prototype reduction

Cited by 7 publications

References 17 publications

Information-assisted volume rendering and visual evaluation through machine intelligence

Information-assisted volume rendering and visual evaluation through machine intelligence

Instance reduction for one-class classification

One-Class Classification through Optimized Feature Boundaries Detection and Prototype Reduction

Contact Info

Product

Resources

About