6 research outputs found
Sparse Reject Option Classifier Using Successive Linear Programming
In this paper, we propose an approach for learning sparse reject option
classifiers using double ramp loss . We use DC programming to find the
risk minimizer. The algorithm solves a sequence of linear programs to learn the
reject option classifier. We show that the loss is Fisher consistent.
We also show that the excess risk of loss is upper bounded by the excess
risk of . We derive the generalization error bounds for the proposed
approach. We show the effectiveness of the proposed approach by experimenting
it on several real world datasets. The proposed approach not only performs
comparable to the state of the art but it also successfully learns sparse
classifiers
Online Active Learning of Reject Option Classifiers
Active learning is an important technique to reduce the number of labeled
examples in supervised learning. Active learning for binary classification has
been well addressed in machine learning. However, active learning of the reject
option classifier remains unaddressed. In this paper, we propose novel
algorithms for active learning of reject option classifiers. We develop an
active learning algorithm using double ramp loss function. We provide mistake
bounds for this algorithm. We also propose a new loss function called double
sigmoid loss function for reject option and corresponding active learning
algorithm. We offer a convergence guarantee for this algorithm. We provide
extensive experimental results to show the effectiveness of the proposed
algorithms. The proposed algorithms efficiently reduce the number of label
examples required
A survey on online active learning
Online active learning is a paradigm in machine learning that aims to select
the most informative data points to label from a data stream. The problem of
minimizing the cost associated with collecting labeled observations has gained
a lot of attention in recent years, particularly in real-world applications
where data is only available in an unlabeled form. Annotating each observation
can be time-consuming and costly, making it difficult to obtain large amounts
of labeled data. To overcome this issue, many active learning strategies have
been proposed in the last decades, aiming to select the most informative
observations for labeling in order to improve the performance of machine
learning models. These approaches can be broadly divided into two categories:
static pool-based and stream-based active learning. Pool-based active learning
involves selecting a subset of observations from a closed pool of unlabeled
data, and it has been the focus of many surveys and literature reviews.
However, the growing availability of data streams has led to an increase in the
number of approaches that focus on online active learning, which involves
continuously selecting and labeling observations as they arrive in a stream.
This work aims to provide an overview of the most recently proposed approaches
for selecting the most informative observations from data streams in the
context of online active learning. We review the various techniques that have
been proposed and discuss their strengths and limitations, as well as the
challenges and opportunities that exist in this area of research. Our review
aims to provide a comprehensive and up-to-date overview of the field and to
highlight directions for future work
Advances in knowledge discovery and data mining Part II
19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p