## 5 Experiments and Results
### Artificial Datasets Test  
After the generation of the Target, Period and Setting artificial datasets presented in Section 3.3, it was necessary to perform tests to identify if they are suitable for application in other experiments of this section. For this, we used the rule learning algorithm ZeroR, which aims to generate the baseline of the classification precision metric. This matches that if the metric of the algorithms intended to be used in the dataset is smaller than that of ZeroR, then it is not indicated or appropriate to use these algorithms. Another objective of the algorithm is to predict the majority class of the dataset, that is, it classifies unclassified data with the class that has the most instances in the training dataset [34]. Table 3 presents the tests performed to generate the baseline with the three artificial datasets, using the validation model cross-validation with the value 10. Table 3 shows that the baseline in all sets was above 70%, which, according to [35] is considered the appropriate mean for testing in WEKA of the precision metric of classification. The majority class in each dataset references notifications that must not be notified to users in the smart environment, indicating that these values have the most in their datasets. Defining the baseline in each dataset, the algorithms delimited in Section 3.1.1 were applied to identify whether they reach the percent of the classification accuracy metric above the baseline of the ZeroR algorithm in each set of artificial data. Figure 8 presents the tests performed by applying the algorithms to the artificial datasets Target, Period, and Setting, and using the validation model cross-validation with the value 10. Presenting Figure 8, we noticed that the percentage of the classification accuracy metric of all algorithms in each dataset were above the baseline generated by the ZeroR algorithm in the previous test seen in Table 3. This indicates that all delimited algorithms are suitable for use in the Target, Period, and Setting sets. After testing with the ZeroR algorithm and identifying the baseline of the classification accuracy metric, tests with the OneR algorithm were performed. This algorithm generates rules based on a single predictor attribute of the applied dataset, that is, it creates rules for each dataset attribute and selects the rules with the lowest error rate as the only rules to use [36]. Table 4 presents the test performed by applying the OneR algorithm to the Target, Period, and Setting artificial datasets, and using the cross-validation model with the value 10. For all sets, the algorithm defined the user attribute as having the lowest error rate among the other predictor attributes and the classify outputs were OutTargetNone, OutPeriodNone, and OutSettingNone respectively for the Target, Period, and Setting sets. However, the generated rules were not consistent with the context of notification management, as all sets were classified that notifications must not be notified to users. Thus, another test, presented in Table 5, was performed with the same configurations of the previous test and using preprocessing to remove the values OutTargetNone, OutPeriodNone, and OutSettingNone of the class attributes of each artificial dataset. Based on Table 5, we obtained rules consistent with the context of notification management compared to Table 4. In the Target dataset, the attribute selected with the lowest error rate inTarget was the most relevant, that is, the algorithm identified notifications that should be notified to certain users are actually notified to them. Therefore, in the Period dataset, the selected attribute was inPeriod, indicating that notifications that arrive in a given period in the environment must be notified in the same period. It just does not happen with the InNight and InDawn values. Finally, in the Setting