The selection of most relevant features can significantly reduce the resource requirement associated with fuzzy ARTMAP neural networks for the classification of high-dimensional data extracted from satellite imagery. This paper introduces a variant of a wrapper-type feature ranking technique that was previously proposed for ARTMAP neural networks by Parsons and Carpenter. As with the originally...

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal co...

In drug design, the knowledge of physical properties of organic compounds is vital. The special field of ADMET (Administration, Distribution, Metabolism, Excretion, Toxicity) involves the determination of properties governing the effect of a drug on the human body, such as aqueous solubility, intestinal absorption and permeability. Assessing these properties experimentally, however, is often la...

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, AR...

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking alg...

This paper shows the potential of neural networks based on the Adaptive Resonance Theory as tools that generate warning signals when bankruptcy of a company is expected (bankruptcy prediction problem). Using that class of neural networks is still unexplored to date. We examined four of the most popular networks of the class – fuzzy, distributed, instance counting, and default ARTMAP. In order t...

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fu...

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include airplane design and manufacturing, automatic target recognition, financial forecasting, machine tool monitoring, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal co...