## 2 Literature Review
### Outlier Detection Method  
Most existing research focuses on developing more accurate models rather than on the importance of data pre-processing. The outlier detection method can be utilized in the pre-processing step to identify inconsistencies in data/outliers; thus, a good classifier can be generated for better decision making. Eliminating the outliers from the training dataset will enhance the classification accuracy. Outlier detection constitutes an important issue for many research areas, including medical, document management, social network, and sensor networks. Several studies have been conducted and showed significant results of outlier detection on improving the classification accuracy. [PERSON] et al. studied text classification in order to improve document management utilizing outlier detection and kNN classifier [47]. The findings indicated that omitting outliers from the training data considerably refined the kNN classifier. In a general case study, [PERSON] and [PERSON] evaluated the outlier effect in classification problems [48]. The study proposed a statistical outlier detection method to determine the outliers based on inter-quartile range (IQR) by classes. The result showed that by partially eliminating the outliers from training dataset, the classification performance of C4.5 was enhanced. Furthermore, in the case of medical application, the outlier detection showed significant result. Past literature used an outlier prediction technique that can enhance the classification performance in the medical dataset [49]. The findings showed that, by eliminating the detected outliers from training set, the classification accuracy was enhanced particularly for Naive Bayes classifier. Finally, past literature builds up a burn tissue classification device to help burn surgeons in planning and executing debridement surgery [50]. The study used the multistage technique to build on \(Z\)-test and univariate analysis to recognize and eliminate outliers from the training dataset. The findings demonstrated that the outlier detection and elimination technique lessened the difference of the training data and enhanced the classification accuracy. Clustering method is a technique that can be used for outlier detection. The clustering method depends on the fundamental supposition that normal cases correspond to big and dense clusters, while outliers make little groups or do not have a place with any cluster whatsoever [51]. DBSCAN is clustering based outlier detection technique that can be utilized to distinguish the outliers [52]. The objective is to recognize the dense regions, which might be calculated by the quantity of objects near a given point. Outliers are the points that do not belong to any cluster. The DBSCAN relies on two important parameters: epsilon (_eps_) and minimum points (_MinPts_). The _eps_ represents the radius of neighborhood about a point x (_\(\varepsilon\)-neighborhood_ of x), while _MinPts_ represents the minimum number of neighbors within the _eps_ radius. Regarding application of DBSCAN for outlier detection, several studies have been conducted and showed significant results in identifying outliers as well as improving the classification result. Past literature showed that by removing noise the quality of real datasets is enhanced [24]. Support vector data description (SVDD) was utilized to classify the dataset. The University of California, Irvine (UCI) dataset has been utilized for the experimental scenario and the proposed method showed an efficient result. In the case of social network, [PERSON] et al. utilized DBSCAN to emphasize community detection. The result showed that the DBSCAN successfully identifies outliers [25]. Eliminating the outliers prompts a precise clustering result that assists with the community identification issue in the area of social network analysis. The DBSCAN-based outlier detection also showed significant results on detecting the outlier sensor data. [PERSON] et al.