Active Learning with an Adaptive Classifier for Inaccessible Big Data Analysis

Supervised machine learning (ML) approaches effectively derive valuable insights from big data. These approaches, on the other hand, require an extensive amount of high quality annotated data for training, created manually by domain experts through a costly and time-consuming process. To overcome this challenge, active learning (AL) is a promising approach, which can support a fast, cost-efficient and common strategy to deal with big data with limited labeling effort. Instead of annotating a large pool of unlabeled data, as in standard supervised learning, AL reduces the volume of data that requires manual annotation by effectively selecting subsets of highly informative samples for manual annotation within an iterative process. In this paper, we aim to present a robust approach utilizing AL to mitigate the aforementioned challenges and help the decision-makers. To be precise, we propose a framework involving a support vector machine (SVM) technique in AL for mining big data to manage inaccessible data situations. The proposed approach is tested on five different semi-supervised data sets. The performance of the proposed framework is evaluated using traditional ML classifiers such as Naïve Bayes (NB), Decision Tree (DT), Sequential Minimal Optimization (SMO), Random Forest (RF), Bagging and Adaboost. Among the reported classifiers, bagging achieves the best outcome, delivering 99.19% accuracy. According to the results of the experiment conducted we find that the proposed method increases the efficiency of the classifiers in AL with fewer training instances.