RNN Based Batch Mode Active Learning Framework
نویسندگان
چکیده
Active Learning has been applied in many real world classification tasks to reduce the amount of labeled data required for training a classifier. However most of the existing active learning strategies select only a single sample for labeling by the oracle in every iteration. This results in retraining the classifier after each sample is added which is quite computationally expensive. Also many of the existing sample selection strategies are not suitable for the multi-class classification tasks. To overcome these issues, we propose an efficient batch mode framework for active learning using the notion of influence sets based on Reverse Nearest Neighbor, which is applicable for multi-class classification as well. To demonstrate the effectiveness of our technique, we compare its performance against existing active learning techniques on real life datasets. Experimental results show that our technique outperforms existing active learning methods significantly especially on multi-class datasets.
منابع مشابه
An Optimization Based Framework for Dynamic Batch Mode Active Learning
Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled data, such algorithms automatically select the salient and representative samples for manual annotation. Batch mode active learning schemes have been recently proposed to select a batch of data instances simultaneously, ra...
متن کاملActive Instance Sampling via Matrix Partition
Recently, batch-mode active learning has attracted a lot of attention. In this paper, we propose a novel batch-mode active learning approach that selects a batch of queries in each iteration by maximizing a natural mutual information criterion between the labeled and unlabeled instances. By employing a Gaussian process framework, this mutual information based instance selection problem can be f...
متن کاملClassification Active Learning Based on Mutual Information
Selecting a subset of samples to label from a large pool of unlabeled data points, such that a sufficiently accurate classifier is obtained using a reasonably small training set is a challenging, yet critical problem. Challenging, since solving this problem includes cumbersome combinatorial computations, and critical, due to the fact that labeling is an expensive and time-consuming task, hence ...
متن کاملA Batch Mode Active Learning for Networked Data
We study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to l...
متن کاملNear-optimal Batch Mode Active Learning and Adaptive Submodular Optimization
Active learning can lead to a dramatic reduction in labeling effort. However, in many practical implementations (such as crowdsourcing, surveys, high-throughput experimental design), it is preferable to query labels for batches of examples to be labelled in parallel. While several heuristics have been proposed for batch-mode active learning, little is known about their theoretical performance. ...
متن کامل