# False Positive RFID Detection Using Classification Models
## 2 Related Work
### Machine Learning and Outlier Detection Models  
Previous studies have successfully applied ANN, LR, SVMs, DT, RF, and XGBoost machine learning models to detect false positives with high classification accuracy using features extracted from tag RSSs [21; 22; 23; 24].  
Outlier detection methods have also been introduced to further improve classification performance for several applications. [PERSON] et al. (2018) utilized LOF to detect outliers in data streams [25]. The proposed model combined LOF and composite nearest neighborhood (CNN) models and was tested on three datasets, provided superior outlier detection compared to other models. [PERSON] et al. (2015) proposed dynamic LOF (D-LOF) for fault detection in a medical sensor network [26]. They utilized LOF to detect outliers and then used fuzzy linear regression for prediction. The proposed model exhibited significantly higher detection accuracy compared to other models. [PERSON] et al. (2011) proposed process monitoring by integrating independent component analysis and LOF [27]. The proposed model exhibited improved accuracy to detect process monitoring faults compared to other models, and was expected to reduce accident risk while simultaneously increasing processing efficiency. [PERSON] et al. (2014) proposed combining multiple subspace principal component analysis and LOF for multimode process monitoring [28]. Experimental results showed that incorporating LOF reduced missed detections and the proposed method outperformed other considered methods. [PERSON] et al. (2018) proposed human activity recognition by combining k-means, LOF, and multivariate gaussiandistribution models [29]. LOF was used to remove outliers with low relative density from each cluster. The proposed model exhibited enhance recognition performance compared to previous studies.  
Interquartile range is another statistical approach that can be utilized for outlier detection. [PERSON] and [PERSON] (2018) evaluated outlier detection methods for predicting energy usage of residential buildings, and showed that employing IQR to detect and remove outliers provided significant model performance improvement in most cases [30]. [PERSON] et al. (2017) compared three sigma, IQR, and median absolute deviation (MAD) outlier identification methods for groundwater state [31]. IQR outlier identification performance was significantly superior to the other two methods. [PERSON] et al. (2016) compared outlier detection methods to detect fraudulent patterns in the healthcare system, and showed that IQR outlier detection was useful to reveal fraudulent providers [32]. [PERSON] et al. (2017) combined IQR and LOF to improve prediction accuracy for public building electricity consumption [33]. The proposed method utilized support vector regression classification model with IQR, LOF, and PCOut outlier detection. IQR and LOF methods provided excellent true outlier detection, consequently improving predicted electricity consumption accuracy (i.e., decreasing root mean square error).  
Thus, many previous studies have established that IQR and LOF can improve classification accuracy across various data fields. Therefore, the present study combined IQR and LOF with machine learning models to improve false positive RFID detection.