## 2 Related Works
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**S.Nns.** & **Title** & **Author** & **Year** & **Insights** & **Remarks** \\ \hline \multirow{2}{*}{1} & Ensemble-learning for data stream analysis: A survey & [PERSON], et al. & 2017 & Provides a survey on different types of ensemble learning for the analysis of data streams. & Survey Paper in which some research problems like global data, handling delayed information and log data not addressed. \\ \hline \multirow{2}{*}{2} & Learning from class-influudanced and expectations & [PERSON], et al. & 2017 & Survey paper about class-influudanced methods. & Survey Paper \\ \hline \multirow{2}{*}{3} & Towards a better understanding of incremental learning & [PERSON], et al. & 2006 & Provides insights on requirements like consistency and conservativeness for incremental learning. & Survey Paper \\ \hline \multirow{2}{*}{4} & Industrial information extraction through multi-phase unstructured documents & [PERSON], et al. & 2018 & 1.1 proposes a two-phase classification approach using consistency of unstructured documents which extracts information based on feature weighting. & Some issues to be addressed are the precision loss and improvement in execution time. \\ \cline{2-2}  & Industrial information extraction through multi-phase unstructured documents & [PERSON], et al. & 2018 & 2.1 induces domain knowledge and handling heterogeneous features. & Same issues to be addressed are the precision loss and improvement in execution time. \\ \cline{2-2}  & Positive and unlabeled learning on categorical data & [PERSON], et al. & 2016 & 2.2 induces a dimension-based algorithm using statistical properties of data to learn categories of classification types by Duke. & Multi-class classification problems is yet to be addressed by Duke. \\ \hline \multirow{2}{*}{6} & Class-specific extreme learning machine for handling binary data imbalance problem & [PERSON], et al. & 2018 & 1.1. CS-H data is a classification algorithm which handles the class-inbalance problem efficiently in heavy data datasets. & Multi-class classification problems is yet to be addressed. \\ \cline{2-2}  & Meta-cognitive online sequential exchange learning machine for data classification & [PERSON], et al. & 2016 & Algorithm proposed for learning multi-class imbalance and concept drift selected by using an adaptive window scheme. & Class imbalance problems in data streams is yet to be addressed by MS-HDM when memory and majority changes gets added over time. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related works. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Title** & **Method** & **Datasets Used** & **Supports Concept Drift** & **Parameters Considered** & **Isoses (Pitter Week)** \\ \hline Industrial information extraction through multi-phase classification using ontology for unstructured documents & Decision Types, Naive Bayes, SVM & Unstructured documents & NO & accuracy & precision loss, performance time \\ \hline Positive and unlabeled learning in categorical data & k-NN & Categorical dataset (CIC Machine Learning Repository) & NO & accuracy & Multi-class \\ \hline Class-specific extreme learning machine for handling binary data imbalance problem & ELM & Binary dataset (kernel dataset) & NO & accuracy & Multi-class \\ \hline Meta-cognitive online sequential exchange machine for imbalanced and concept-affinity & ELM & Binary and multi-class datasets & YES & accuracy & Class imbalance over time. \\ \hline Proposed work (CIIII) & ELM & Binary and multi-class datasets & NO & accuracy, performance time & Concept drift \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the related works and _CIIII_.