Visualization of classifier decisions in multi-D spaces
Visualization of classifier decisions in a feature space help us to understand its behavior. The visualization is straightforward when our data has only two features. But what about multi dimensional problems?
PRSD Studio provides visualization of classifier decisions in feature spaces with more than two dimensions.
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Tutorial example on protecting classifier from outliers
Often, we need to protect a trained classifier from accepting outliers examples appearing in production. This tutorial shows how to achieve this by addding a rejection option to a trained discriminant with sdreject command. Construction of interactive reject curve is also illustrated.
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Detailed example on adding reject option is available in the Knowledge Base
(0) CommentsASCI course 2010
Last week, we lectured in the Advanced Pattern Recognition Course organized by TU Delft within the Advanced School for Computing and Imaging (ASCI school). 
The course is offered to PhD students that are interested in the field of Pattern Recognition or that are already experienced and would like to deepen and widen their understanding of the field. This course has been more then fully booked, with 30 participants from all corners of The Netherlands. Our lectures have focused on evaluation, ROC analysis and classifier optimization, leading to an integrated approach for system design. It has been a pleasure for us to meet so many bright students and learn about their interesting projects. We wish them success and fun in their research!
Tutorial example on optimizing three-class classifiers with ROC analysis
Sometimes, one of the classes in multi-class problem is much larger than the remaining classes. Classifiers, trained in such imbalanced problem, usually deliver very poor performances with a default decision function. The reason is that the model output of the large class dominates the solution. The default procedure of making decisions assumes that all the classes are equally important which results in high misclassification of small classes.
PRSD Studio allows you to quickly optimize multi-class classifiers in imbalanced problems. Watch the video inside!
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How to quickly rename classes or define meta-classes?
When designing classifiers, we often need to define new classes by renaming existing ones. sdrelab command allows us to do just that quickly and easily.
Class relabeling helps us to define meta-classes, to compare data sets before and after normalization or to understand where the data of a specific sub-class/patient/cluster fits with respect to others.
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Presenting our research on ROC hierachical classifiers at NVPHBV meeting
We have presented our research on optimization of hierarchical classifiers at the spring meeting of NVPHBV (Dutch society for pattern recognition and image processing).
Complex problems are often easier to handle if decomposed into sub-problems and tackled independently. Hierarchical classifiers offer a great tool for such decomposition but are difficult to optimize according to application requirements. This is a serious problem we encounter daily in our industrial projects. In our talk, we described our approach allowing the designer to perform cost-sensitive ROC optimization for apriori-defined hierarchical classifiers.
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How to setup leave-one-patient out cross-validation
In many applications, we need to make sure our classifier generalizes to unseen patients, object events etc. Therefore, we need to consider these entities in cross-validation of our algorithm. PRSD Studio provides leave-one-object-out using the sdcrossval routine. But in this example, we show how to make a very simple leave-one-object-out scheme in two lines of code where everything is open to our direct understanding.
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Selecting a random subset based on a specific set of labels
Often, we need to generate a random subset of samples using a specific set of labels. For example, in the medical problem we may be interested to sample not from the top-level disease/no-disease classes by from each patient or from each tissue type. randsubset helps you to do just that in a simple way.
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Live feature distributions in scatter plots
The 2.2.1 release brings new interactive tool into the sdscatter: the feature distribution plot. It shows the histogram for each class for the currently selected feature on the horizontal and vertical axis of the sdscatter. This gives us better understanding of true nature of class overlap especially in large data sets where traditional scatter is very cluttered.
The feature plots are updated live with scatter operations (showing class subsets, hiding classes, painting labels).
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Protecting clusters from outliers using reject option
Often, when trying to understand our data with cluster analysis we wonder where will the new examples map. Many types of cluster analysis techniques such as k-means or mixtures of Gaussians allow us to apply the trained clustering on new data because they, in fact, train a classifier.
But these trained clustering models act as discriminants. That means that they assign every new data sample into one of the found clusters. This includes the samples very distinct from anything encountered when performing the cluster analysis.
Would not that be great if we could identify samples sticking out from our clustering?
That’s exactly what you may now do using the sdreject command introduced in PRSD Studio 2.1. We may simply add a reject option to a trained clustering model and so protect it from outliers.
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