We consider the multi-label classification problem in this paper. We propose a randomized ensemble learning algorithm, random tag forest, which is an ensemble of random tag trees. Each tree is built by randomly defining a hierarchical tree structure over a subset of tag vocabulary. Each node in the tree corresponds to a tag in the vocabulary. During testing, a testing example will pass through ...

Classification is the process of assigning a class label to an observation based on its proprieties or attributes. A classification algorithm is applied to a data set, producing a model. By studying the model, insights about the data set structure can be gained. The benefits that a model can bring depend on the model. In this work, a Random Forest model is used for the analysis of data. A Rando...

Modern biology has experienced an increasing use of machine learning techniques for large scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest (RF) [6] technique, which includes an ensemble of decision trees and incorporates feature selection and interactions naturally in the learning process, is a popular choice. It is nonparametric, interpretable, effic...

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified average feature acquisition budget. While random forests yield strong generalization performance, they do not ex...

When individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a Fuzzy Random Forest, is proposed. This ...

The Random forest model for machine learning has become a very popular data mining algorithm due to its high predictive accuracy as well as simiplicity in execution. The downside is that the model is difficult to interpret. The model consists of a collection of classification trees. Our proposed visualization aggregates the collection of trees based on the number of feature appearances at node ...

A classification tree represents the probability spaceP of posterior probabilities p(y|x) of label given feature by a recursive partition of the feature space X , where each partition is performed by a test on the feature x called a split rule. To each set of the partition is assigned a posterior probability distribution, and p(y|x) for a feature x ∈ X is then defined as the probability distrib...

Medical imaging protocols produce large amounts of multimodal volumetric images. The large size of the datasets contributes to the success of supervised discriminative methods for semantic image segmentation. Classifying relevant structures in medical images is challenging due to (a) the large size of data volumes, and (b) the severe class overlap in the feature space. Subsampling the training ...

DNA Image Cytometry is a method for early cancer diagnosis and grading of cancer, using a photomicroscopic system to measure the DNA content of nuclei. Specifically for the prostate, this method can be used to distinguish between clinically insignificant, non-aggressive tumors, and those which need to be removed or irradiated. This decision is based on the analysis of the DNA distribution among...