## 2 Related Works  
Many researchers have contributed their works in the area of classification of unstructured data and incremental learning [8]. This section highlights some works contributed in this area. [PERSON], et al. conducted a research survey on the analysis of data streams using ensemble learning [16]. Data streams collected from the information system applications use learning algorithms which acts in dynamic environments. Ensemble learning plays an important role in data stream analysis, classification and regression tasks to handle the non-stationary environments. Various categories of ensemble learning [17] are available based on different criteria. For supervised learning task of classification, the available types of ensembles are, (1) stationary or non-stationary learning, (2) chunk or online ensembles, (3) active or passive ensembles addressing the drifts. Ensemble Learning also addresses advanced issues like class-imbalanced learning, novelty detection, active and semi-supervised learning and more complex data structures. Some of the open research problems not addressed are: (1) handling delayed information, (2) gradual drifts which are more complex to be detected than sudden changes, (3) considering background knowledge while classifying data streams, (4) ensemble pruning methods, and (5) handling big data and privacy issues. [PERSON], et al. contributed to a review paper which discusses the different methods of learning from class-imbalanced data [18] and their applications. Detection of rare events which potentially impact the society negatively is viewed as a prediction task in data mining and machine learning communities. Since these events occur rarely, they suffer from the problem of imbalanced data. Ensemble classifiers are used for the resampling of clinical data in the area of chemical and biomedical engineering, in case of high-dimensional data, feature selection techniques are adopted. The goal of the prediction in these tasks is basically profit-driven instead of accuracy-driven, which is known as cost-sensitive learning. Detection of rare events in the network log and unstructured data needs data cleaning and feature engineering processes, which are not addressed in this paper. [PERSON], et al. presented a study that gives insights on the natural requirements such as consistency and conservativeness [19] for incremental learning [20]. Learning from positive examples as well as from both positive and negative examples were analyzed to understand the natural requirements of incremental learning. A case study of regular pattern languages was analyzed to show how the learnability of the pattern languages gets affected by the conditions like consistency and conservativeness when using natural hypothesis spaces for learning. When considering the consistency, normalization of the iterative learners can be achieved in local consistency. In contrast, the capabilities of iterative learners get reduced in global consistency. The demands of the local and global conservativeness are equal when considering the iterative learners in case of learning from only positive examples. A global conservative learner can iteratively learn classes from positive and negative examples, whereas a local conservative learner cannot learn iteratively. The results got from the study helps to characterize the structure of typical classes which can be learned incrementally and also elaborates on uniform incremental learning methods [21]. [PERSON], et al. proposed a novel method for extracting industrial information through multi-phase classification using ontology [22] of the unstructured documents. The efficiency of the existing information extraction approaches is unsuitable for domain-specific applications which demand semantic and contextual taxonomy. This proposed method performs a two-phase classification approach which extracts information based on feature weighting. The first phase performs a sentence classification followed by the phase of word classification.