

![0_image_1.png](0_image_1.png)

![0_image_2.png](0_image_2.png)

![0_image_0.png](0_image_0.png)

![0_image_3.png](0_image_3.png)

![0_image_4.png](0_image_4.png)

## 4.2 Aljezur Dataset Results

With this dataset we achieved even better results. The use of SPPAM before the training task improved the classification performance on almost all analyzed metrics. In some cases, we get 10% gain on the correctly classified instances percentage. These results were statistical significant for BayesNet using K2 and TAN, SimpleCart, ZeroR, SMO and DTNB.

For the metrics where the results were not improved (Naive Bayes and DTNB), the difference is not significant.

In Figure 4 we show the difference between the averages of correctly classified instances percentage for the original dataset and the SPPAM-preprocessed dataset for the Aljezur dataset. Here we can see graphically how better in average, the classification algorithms can correctly classify new instances using our method. The values are also in percentage.

## 5 Conclusions And Future Work

In this work, we proposed a simple, general solution to the problem of learning classifiers for multiple correlated data such as multiple exams of a single patient, internet customer preferences, weather forecast, sea prediction, among others. SPPAM, a Statistical PreProcessing AlgorithM, takes the original dataset containing related data, and produces a new dataset with all correlated data aggregated using metrics such as maximum, minimum, average, etc. We tested SPPAM on two datasets that contain records associated according to a date. We used WEKA to train on the original datasets and on