Data Quality Enhancement Technology to Improve Decision Support - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 75

Based on Figure 5, the release desired colored in blue (real data) and release obtained
colored in red (predicted data) are not close to each other. The difference is due to the low
quality of the data that contains noise and outliers, affecting the membership function and
causing the function to be inaccurate and not robust. The membership function has two
parameters, namely the mean and standard deviation, which are tuned based on the data.
3.2 Analysis and results using proposed method
After removing the missing value, FCM with statistic equation are applied to detect outliers
and remove noisy data on the Italy dataset, the desired clustered data was extracted and
entered as an input entry into the Type-1 Fuzzy Logic System with gradient descent
algorithm to tune membership function parameters. The result shows a different type of
graph that presenting the method effect to the dataset.
Fig. 6. Proposed Method on Italy Dataset
Figure 6 shows the output of Proposed. The attribute is a temperature based on time
(second). A small scale of data (desired clustered data) was used to show the system
behaviour (Proposed Method).
Blue shows the desired data or the real data and red is the obtained or predicted data. If
data before the contribution (Type-1 FLS) were compared, it resulted much better and
represents predicted data more closely to the real ones.
3.3 Accuracy measurements
To evaluate the results we used standard measurement called Mean Absolute Error (MAE)
and Root Mean Square Error (RMSE) which formulated as below: