Batch-mode Active Learning Technique Research Articles

A fast partition-based batch-mode active learning technique using SVM classifier

The selection of informative samples known as query selection is the most challenging task in active learning. In this article, a batch-mode active learning technique is presented by defining a novel query function. The proposed technique first divides the unlabeled samples into uniform partitions in one-dimensional feature space according to their distribution in the original feature space. Then to select the most informative samples from the unlabeled pool, one sample from each partition is selected based on an uncertainty criterion defined by exploiting SVM classifier. The number of unlabeled samples selected at each iteration of active learning is determined automatically and depends on the number of non-empty partitions generated. The effectiveness of the proposed technique is measured by comparing it with four state-of-the-art techniques exist in the literature by using four different UCI repository data sets. The experimental analysis proved that the proposed technique is robust and computationally less demanding.

Adaptive Batch Mode Active Learning Technique Using an Improved Time Adaptive Support Vector Machine for Classification of Remote Sensed Image Applications

Adaptive Batch Mode Active Learning Technique Using an Improved Time Adaptive Support Vector Machine for Classification of Remote Sensed Image Applications

A batch-mode active learning SVM method based on semi-supervised clustering

A batch-mode active learning technique taking advantage of the cluster assumption was proposed. It focused on binary classification tasks adopting SVM support vector machine. In each active learning iteration, unlabeled instances in the SVM margin were first grouped into two clusters. Then from each cluster, points most similar to the other cluster were selected for labeling. Such points lying near the boundary between clusters were expected to become support vectors in the final classification model with high probability. The clustering process was performed in the same kernel space as SVM. With semi-supervised K-medoids, labeled instances were also used to improve the clustering performance. Experiments showed that the proposed method was efficient and robust to poor initial samples.

A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier

In this letter, we present a novel batch-mode active learning technique for solving multiclass classification problems by using the support vector machine classifier with the one-against-all architecture. The uncertainty of each unlabeled sample is measured by defining a criterion which not only considers the smallest distance to the decision hyperplanes but also takes into account the distances to other hyperplanes if the sample is within the margin of their decision boundaries. To select batch of most uncertain samples from all over the decision region, the uncertain regions of the classifiers are partitioned into multiple parts depending on the number of geometrical margins of binary classifiers passing on them. Then, a balanced number of most uncertain samples are selected from each part. To minimize the redundancy and keep the diversity among these samples, the kernel <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering algorithm is applied to the set of uncertain samples, and the representative sample (medoid) from each cluster is selected for labeling. The effectiveness of the proposed method is evaluated by comparing it with other batch-mode active learning techniques existing in the literature. Experimental results on two different remote sensing data sets confirmed the effectiveness of the proposed technique.

A cluster-assumption based batch mode active learning technique

In this paper, we propose an active learning technique for solving multiclass problems with support vector machine (SVM) classifiers. The technique is based on both uncertainty and diversity criteria. The uncertainty criterion is implemented by analyzing the one-dimensional output space of the SVM classifier. A simple histogram thresholding algorithm is used to find out the low density region in the SVM output space to identify the most uncertain samples. Then the diversity criterion exploits the kernel k-means clustering algorithm to select uncorrelated informative samples among the selected uncertain samples. To assess the effectiveness of the proposed method we compared it with other batch mode active learning techniques presented in the literature using one toy data set and three real data sets. Experimental results confirmed that the proposed technique provided a very good tradeoff among robustness to biased initial training samples, classification accuracy, computational complexity, and number of new labeled samples necessary to reach the convergence.