# False Positive RFID Detection Using Classification Models
## 3 Dataset and Feature Extraction
The data gathering program (Figure 2c), received RSS for each tag through the gathering session, and the raw data was stored as a CSV file. For true positive readings, the worker performed multiple gathering sessions, moving the tags through the gate, and RSS values for static tags located within read range were also collected to generate false positives. Different movement paths and speeds were also performed for tags moved close to the gate. Table 2 showed the detail execution parameters for gathering the true positive readings (tag movements through the gate), such as number of boxes/tags and the speed and movement pattern of trolley. During gathering session, different numbers of boxes were loaded on the trolley and different numbers of movements through the gate were conducted. In addition, different speeds and paths of the trolleys moving through the gate were considered for data gathering. Based on Table 2, most of the experiments were performed with four tagged boxes on the trolley, with the speed between 0.5-0.99 m/s, and moves straight ahead. In our experiment, the distance between start point (when the tag is first read) and end point (last occurrence of tag) was approximately 4 m.  
Figure 4 shows that the raw RFID data, recorded for each gathering session, consisted of the electronic product code (i.e., the tag ID), date and time, antenna information, read time, and RSS. Each raw RFID dataset was then divided based on the tag ID, statistical features to represent data characteristics, and class attributes, labelled as either true positive (1) or false positive (0). Table 3 compares classification accuracy for individual features based on decision stumps (decision trees with single input features). Although each feature can identify true and false positives, none of the features achieved accuracy >90% individually. Thus, a single predic \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline Min & Minimum signal strength during a gathering session \\ Max & Maximum signal strength during a gathering session \\ Mean & Average signal strength during a gathering session \\ Var & RSS variance during a gathering session \\ Std & RSS standard deviation during a gathering session \\ Skewness & Distribution asymmetry \\ Kurtosis & Indicates if the data distribution is heavy- or light-tailed, relative to normal \\ Diff & Difference between highest and lowest signal strength during a gathering session \\ Count & Total number of reads for the tag during a gathering session. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attributes extracted from RSS. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Frequency** \\ \hline \multirow{6}{*}{Number of boxes/tags} & 1 & 17 \\  & 2 & 15 \\  & 3 & 12 \\  & 4 & 53 \\  & 5 & 11 \\  & 6 & 24 \\ \hline \multirow{6}{*}{Speed of trolley (m/s)} & \textless{}0.50 & 84 \\  & 0.50–0.99 & 216 \\ \cline{1-1}  & 1.00–1.50 & 155 \\ \cline{1-1}  & 1.51–2.00 & 34 \\ \cline{1-1}  & \textgreater{}2.00 & 5 \\ \hline \multirow{2}{*}{Movement pattern} & Straight ahead & 410 \\ \cline{1-1}  & Diagonal line & 84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Execution parameters for gathering the true positive readings.