|dc.description.abstract||The many complex problems facing researchers and engineers demand innovative solutions.
Machine learning techniques are growing in popularity due to their versatility and power.
However, challenges remain. Popular machine learning algorithms such as Artificial Neural
Networks are difficult to train, and require many designer choices that heavily impact the
performance of the network. Furthermore, the randomized starting point of most ANN variants
means that even if optimal choices are made, it may still take multiple trials to obtain satisfactory
results. Fuzzy Systems are also widely used, but cannot tackle high dimensional problems or
produce outputs of similar quality to neural networks. A novel defuzzification routine based on
cubic splines seeking to improve the performance of FS is introduced, and compared to many
state of the art machine learning techniques. The experimental results show the proposed
algorithm performs competitively with popular machine learning methods, while not requiring a
lengthy training process.||en_US