## 4 Empirical Study
### Feature Selection
#### 4.2.1 Exploring Event Logs: Feature Subset Generation Based on Ranking Method  
We examine the practical usefulness of following commonly used methods including CFS (Correlation-based Feature Selection), IG (Information Gain), GR (Gain Ratio), Chi (Chi-squared) and OneR to rank the features, in other words, features in the initial feature set and output different ranking results. These feature ranking methods are statistical and entropy-based [26], with good performance in various domains [27]. In Table 3, a lower number means a more relevant feature for cargo theft fraud.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & **CFS** & **IG** & **GR** & **Chi** & **OneR** \\ \hline
**1–Age** & 8 & 7 & 9 & 8 & 10 \\
2–Education Background & 34 & 34 & 34 & 34 & 34 \\
3–Credit _in Port_ & 14 & 16 & **15** & 15 & 12 \\
4–Region & 32 & 33 & 31 & 27 & 29 \\
5–Cargo Type & 18 & 17 & **16** & 16 & 19 \\
6–Trade Type & 30 & 29 & 28 & 33 & 3 \\
7–Import or Export & 38 & 38 & 38 & 38 & 35 \\
8–Customs Supervision & 7 & 5 & 7 & 5 & 9 \\
9–Cargo Price & 33 & 32 & 33 & 32 & 28 \\
10–Trick Manufacturer & 19 & 19 & 21 & 21 & 20 \\
11–Year of Manufacture & 9 & 10 & **10** & 11 & 13 \\
12–Registration Region & 27 & 31 & 32 & 30 & 33 \\
13–Trick Category & 12 & 13 & **13** & 14 & 14 \\
14–Rodemal or Not & 1 & 3 & 3 & 3 & 5 \\
15–Ownership & 26 & 28 & 29 & 29 & 31 \\
16–Time of Filing & 15 & 18 & **17** & 18 & 18 \\
17–Bidacklist & 11 & 12 & **11** & 9 & 8 \\
18–Mart Category & 6 & 6 & 6 & 7 & 4 \\
19–Specialized Yard & 21 & 25 & 24 & 25 & 24 \\
20–Cargo Volume & 29 & 26 & 25 & 24 & 27 \\
21–Congior Level & 13 & 11 & **12** & 13 & 17 \\
22–Contractor Category & 20 & 22 & 19 & 19 & 26 \\
23–Means of Payment & 5 & 8 & **4** & 4 & 6 \\
24–Arrival Time & 2 & 2 & 2 & 6 & 1 \\
25–Leave Time & 22 & 20 & 20 & 22 & 21 \\
26–Operation Period & 4 & 4 & 5 & 2 & 7 \\
27–Operation Period Deviation & 37 & 36 & 36 & 36 & 36 \\
28–Operation Period Fluctuation & 28 & 30 & 26 & 28 & 30 \\
29–Cargo Weight Deviation & 3 & 1 & **1** & 1 & 2 \\
30–Cargo Weight Fluctuation & 31 & 27 & 30 & 31 & 32 \\
31–Weighbridge of Empty Cargo Truck & 35 & 35 & 35 & 35 & 36 \\
32–Weighbridge of Heavy Cargo Truck & 36 & 37 & 37 & 37 & 38 \\
33–Arrival Date & 24 & 24 & 22 & 20 & 22 \\
34–Shift Category & 25 & 23 & 27 & 26 & 23 \\
35–Number of Workers & 17 & 15 & 18 & 17 & 16 \\
36–Number of Machines & 23 & 21 & 23 & 23 & 25 \\
37–Weather & 10 & 9 & **8** & 10 & 15 \\
38–Degree of Bussussuss & 16 & 14 & **14** & 12 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature ranking results.  
Based on the feature rankings in Table 3, we find that:  
* Different ranking results are obtained by using various feature ranking methods. Some of these outcomes are similar, especially using IG, GR and Chi.
* The features 'Cargo Weight Deviation', 'Reformed or Not', and 'Arrival Time' always rank the highest independent of the method used. This means that these three features are closely relevant to the theft risk of bulk cargo.
* The features 'Education Background', 'Weighbridge of Empty Cargo Truck', 'Weighbridge of Heavy Cargo Truck' and 'Operation Period Deviation' always rank the lowest. This means that these four features are the most irrelevant features to the theft risk of bulk cargo.  
Finally, feature subsets can be generated based on the how relevant the feature is to the theft risk of bulk cargo. We have 38 features in the initial feature set, so 38 feature subsets are generated according to the feature ranking result of each ranking method and the top \(i\) relevant features constitute multiple subsets, respectively (Figure 2). For instance, the feature ranking results based on the GR method are shown in bold and italic in Table 3 and 38 feature subsets are generated including S1 = [R29, R24], S3 = [R29, R24, R14], S4 = [R29, R24, R14, R23],..., S38 = [R1, R2,..., R38]. Hence, evaluating and selecting the most effective subset of features for classification is the next task.