# False Positive RFID Detection Using Classification Models
###### Abstract  
Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners. RFID; RSS; machine learning; classification; false positive; outlier detection +
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Since WMSs should correctly calculate the total amount of tagged products loaded, detecting and filtering false positives is critical to ensure the correct delivered product records are retained. Received signal strength (RSS) is the relative quality of a received signal for a device, which is related to the distance between the tags and antenna for RFID, and has been previously utilized for indoor localization systems (ILSs) [13; 14; 15; 16], indoor mapping and navigation in retail stores [17], indoor tracking system for elderly [18], and gesture recognition [19]. Previous studies confirm RSS approaches to be suitable to detect false positives [20; 21; 22], where static and moved tags were classified as false positive and true positive readings, respectively. Machine learning models have also been utilized to successfully detect false positives using RSS based features as inputs. However, some retail store-based studies showed that tag movement close to the gate and through the gate were classified as false positive and true positive readings, respectively [23; 24]. Therefore, false positive classification must consider other tag movement types, such as tag movement close to the gate, as well as static tags for WMSs. Collected data commonly includes outliers, due to environmental interference and imperfect sensing, and various outlier detection methods have been proposed to detect and remove them. The local outlier factor (LOF) has been applied for several application areas to detect outliers in data streams [25], fault detection in medical [26] and process monitoring [27; 28], and activity recognition [29]. Interquartile range (IQR) has shown significant outlier detection capability for residential building energy usage [30], groundwater data [31], and health care insurance fraud [32]. LOF and IQR have both been implemented to predict electricity consumption, providing good performance detecting true outliers [33]. Thus, outlier detection helps improve classification model performance. The present study investigated machine learning model performance to detect false positives based on tag RSS. To represent complex warehouse situation, we considered false positives for not only static but moving tags close to the gate. Incorporating outlier detection models within the machine learning algorithms significantly improved false positive prediction performance. We also demonstrated that the proposed classification model could be integrated into real-time monitoring systems (RTMSs), ensuring false positives were filtered and hence not stored in the database. The remainder of this paper is organized as follows.