On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems
This work addresses scalability issues in fuzzy decision trees for Big Data classification, offering an incremental improvement in efficiency.
The paper tackles the complexity of multi-way fuzzy decision trees in Big Data classification by introducing a distributed fuzzy partitioning method that uses the probability integral transform to adjust fuzzy sets to data distributions, resulting in up to 6 million fewer leaves while maintaining classification accuracy.
We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step process is applied : 1) transformation of the original distribution into a standard uniform distribution by means of the probability integral transform. Since the original distribution is generally unknown, the cumulative distribution function is approximated by computing the q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy partition in the transformed attribute space using a fixed number of equally distributed triangular membership functions. Despite the aforementioned transformation, the definition of every fuzzy set in the original space can be recovered by applying the inverse cumulative distribution function (also known as quantile function). The experimental results reveal that the proposed methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT) induction algorithm to maintain classification accuracy with up to 6 million fewer leaves.