Selected article from IEEE Transaction on Fuzzy Systems
Improving Supervised Learning Classification Methods Using Multigranular Linguistic Modeling and Fuzzy Entropy
We propose to apply linguistic modeling methods in order to obtain a linguistic representation. With the help of multigranular linguistic modeling, data can be transformed and expressed using different (unbalanced) linguistic label sets. Expressing the data using linguistic expressions instead of numbers increases the readability and reduces the complexity of the problem, and data recovering methods allow us to manually control the level of precision. In this paper, several datasets are transformed and utilized for classification tasks using several supervised learning algorithms. For each combination of datasets and algorithms, the data have been expressed using several linguistic label sets that have different granularity values. After carrying out the testing processes, we can conclude that, in some cases, reducing data complexity leads to better classification results. Therefore, it is found that linguistic representation of the training data with just the necessary and sufficient precision can improve the reliability of the classification process.
IEEE Transactions on Fuzzy Systems, Oct. 2017