Agarwood classification: A Case-Based Reasoning approach based on E-nose

Using an array of sensors (E-nose) to classify Agarwood has proven to be successful and produced performance close to an expert level (90% of expert level performance) but it has proven difficult to eliminate misclassifications without over-fitting. In our effort to improve our result we explored a self-improving Case-Based Reasoning approach and reached 100% correct classification. Case-Based Reasoning is an approach that will learn from every new classified case and hence the risk for misclassification is reduced. Also when new cases have to be classified that have never occurred before the system will avoid misclassification (similarity measurement is low). The approach also enables indeterminism; in reality a sample may be both close to a good case and a bad case and need further exploration by experts. The approach also handles natural variants in the wood samples well; both low-quality and high-quality samples may spread considerably in the context of E-nose readings and there is no model available of low or high quality.