Classification from only positive and unlabeled functional data

Learning Classifiers from Imbalanced, Only Positive and Unlabeled Data Sets

In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This contest consists of two classification tasks based on data from scientific experiment. The first task is a binary classification task which is to maximize accuracy of classification on an evenly-distributed test data set, given a fully labeled imbalanced training data set. The second task is also ...

Positive Unlabeled Learning for Data Stream Classification

Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data i...

Text Classification from Positive and Unlabeled Examples

Assessing binary classifiers using only positive and unlabeled data

Assessing the performance of a learned model is a crucial part of machine learning. Most evaluation metrics can only be computed with labeled data. Unfortunately, in many domains we have many more unlabeled than labeled examples. Furthermore, in some domains only positive and unlabeled examples are available, in which case most standard metrics cannot be computed at all. In this paper, we propo...

Classification from Pairwise Similarity and Unlabeled Data

One of the biggest bottlenecks in supervised learning is its high labeling cost. To overcome this problem, we propose a new weakly-supervised learning setting called SU classification, where only similar (S) data pairs (two examples belong to the same class) and unlabeled (U) data are needed, instead of fully-supervised data. We show that an unbiased estimator of the classification risk can be ...