Machine Learning
DNA hypernetworks for information storage and retrieval
DNA'06 Proceedings of the 12th international conference on DNA Computing
A bayesian algorithm for in vitro molecular evolution of pattern classifiers
DNA'04 Proceedings of the 10th international conference on DNA computing
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Evolutionary hypernetworks for learning to generate music from examples
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
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We present a biology-inspired probabilistic graphical model, called the hypernetwork model, and its application to medical diagnosis of disease. The hypernetwork models are a way of simulated DNA computing. They have a set of hyperedges representing a subset of features in the training data. These characteristics allow the hypernetwork models to work similarly to associative memories and make their learning results more understandable. This comprehensibility is one of main advantages of the models over other machine learning algorithms such as support vector machines and artificial neural networks which are used in a wide range of applications but are not easy to understand their learning results. Since medical applications require both competitive performance and understandability of results, the hypernetwork models are suitable for this kind of applications. However, ordinary hypernetwork models have limitations that hyperedges cannot be changed after they are sampled once. To improve this diversity problem, we adopted simple evolutionary computation method, the hyperedges replacement strategy as the method of keeping the diversity into conventional hypernetworks in addition to error correction for model learning. To show the improvement, we used aptamer-based cardiovascular disease data. Experiment results show that the hypernetworks can achieve fairly competitive performance and the results are also comprehensible.