Using Neural Networks to Include Semantic Information into Classification

The accuracy of pattern-classification methods depends on how well the measured characteristics (i.e., features) represent the object to be classified. When using pre-designed features as it is the case of many pattern classifiers, one can try to enhance the features' discriminative power by inserting high-level semantic information into the feature vectors. In this paper, we propose a method that increases the discriminative power of features by augmenting them with high-level semantic information learned from training data. Our method combines the advantages of dimensionality reduction techniques and feature-selection techniques. Instead of augmenting feature vectors, we map them using a modified neural network that has been trained to categorize the data into target groups. This neural network embeds categorization information. We tested the method on classification tasks for pollen species, human action, and acoustic signals. In all these tasks, our feature-enhancing method improved classification rates.