## 5 Conclusions  
In real-world practices, data preprocessing is always tricky for analysts, mainly when dealing with class imbalance, missing data, and inconsistent distributions. This study considered the problem of predicting binary class labels from sensor readings with missing data based on a publicly available APS dataset. First, the IR was introduced to determine if the assumption of a consistent distribution of the class labels across the training and test sets was satisfied for the considered APS dataset and the attributes were found to be not useful were removed.  
Next, a sorted missing percentage technique was proposed to construct a control and several experimental groups of attributes to be used in training of several classifiers for comparison. According to the experimental results, the LR model trained using the EXP2 attribute group (where the attributes with over 20% of missing values were filtered out) demonstrated the best performance, achieving an accuracy score of 99.56%, F-measure of 73.24%, and MCC of 74.30%. Further, the relative \(\beta\) values and \(X_{c}\) indicators based on SDT were used to compare the distinguishing ability of the best performing LR model depending on the attribute group. The best values of \(\beta\) = 0.0013, \(\beta_{opt}\) = 0.0044, and \(X_{c}\) = \(-\)1.8994 were once again achieved through the LR and EXP2 combination, which outperformed other methods  
Figure 5: ROC curves and AUC values of EXP2-LR for the POS class label: (**a**) training set and (**b**) test set.  
reported in the literate and applied to the same APS dataset. These results demonstrated that the proposed sorted missing percentage technique allows one to address the problem of missing data in sensor readings without changing the original data structure and then builds a predictive model with a low response bias and a high distinguishing ability.  
Although the original APS dataset does not provide rich information, e.g., no complete definitions of the attributes, this empirical study and the presented numerical results demonstrated that a method involving the IR check and sorted missing percentages could provide a flexible way to deal with missing sensor data.  
Future work can proceed in the following two directions. First, the proposed method can be applied to other datasets to verify if it can generalize well at instances wherein the data source may comprise of different scenarios, e.g., when missing data is only encountered in the training set. The research materials including data files and the deployment scripts were compressed and stored in a cloud hard drive, [[https://drive.google.com/drive/folders/1E_Rxt20L6](https://drive.google.com/drive/folders/1E_Rxt20L6) dDRxrdlCGgPhCRGkfaf9 MZ]([https://drive.google.com/drive/folders/1E_Rxt20L6](https://drive.google.com/drive/folders/1E_Rxt20L6) dDRxrdlCGgPhCRGkfaf9 MZ). Second, other attribute selection methods, advanced indicators of performance, and the innovated machine learning modeling techniques such as artificial intelligence networks and deep learning, can be tested to see if they can generate more reliable and accurate results than those achieved in this paper to satisfy the demands of real-world applications.  
[PERSON], [PERSON] and [PERSON] designed the study. [PERSON] was responsible for methodology design and data analysis. [PERSON], [PERSON] and [PERSON] reviewed relevant literature and interpreted the acquired data. [PERSON], [PERSON] and [PERSON] drafted the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.