Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy

Abstract

Obtaining good classification results using supervised learning methods is critical if we want to obtain a high level of precision in the classification processes. The training data used for the learning process plays a very important role in achieving this objective. Therefore, it is important to represent the data in a way that best expresses its meaning. For this purpose, we propose to apply linguistic modeling methods in order to obtain a linguistic representation. With the help of multi-granular linguistic modelling, data can be transformed and expressed using different (unbalanced) linguistic label sets. Expressing the data using linguistic expressions instead of numbers increases the readability, 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 has 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.