Criteria for evaluating the classification reliability of a neural classifier and for accordingly making a reject option are proposed. Such an option, implemented by means of two rules which can be applied independently of topology, size, and training algorithms of the neural classifier, allows one to improve the classification reliability. It is assumed that a performance function P is defined which, taking into account the requirements of the particular application, evaluates the quality of the classification in terms of recognition, misclassification, and reject rates. Under this assumption the optimal reject threshold value, determining the best trade-off between reject rate and misclassification rate, is the one for which the function P reaches its absolute maximum. No constraints are imposed on the form of P, but the ones necessary in order that P actually measures the quality of the classification process. The reject threshold is evaluated on the basis of some statistical distributions characterizing the behavior of the classifier when operating without reject option; these distributions are computed once the training phase of the net has been completed. The method has been tested with a neural classifier devised for handprinted and multifont printed characters, by using a database of about 300000 samples. Experimental results are discussed.

Semi-supervised learning is a promising research area aiming to develop pattern recognition tools capable to exploit simultaneously the benefits from supervised and unsupervised learning techniques. These can lead to a very efficient usage of the limited number of supervised samples achievable in many artificial olfaction problems like distributed air quality monitoring. We believe it can also be beneficial in addressing another source of limited knowledge we have to face when dealing with real world problems: concept and sensor drifts. In this paper we describe the results of two artificial olfaction investigations that show semi-supervised learning techniques capabilities to boost performance of state-of-the art classifiers and regressors. The use of semi-supervised learning approach resulted in the effective reduction of drift-induced performance degradation in long-term on-field continuous operation of chemical multisensory devices.