NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
Monotonic Optimization: Problems and Solution Approaches
SIAM Journal on Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
Derivation of monotone decision models from noisy data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Uncertainty Estimation Using Fuzzy Measures for Multiclass Classification
IEEE Transactions on Neural Networks
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In many classification and prediction problems it is known that the response variable depends on certain explanatory variables. Monotone neural networks can be used as powerful tools to build monotone models with better accuracy and lower variance compared to ordinary nonmonotone models. Monotonicity is usually obtained by putting constraints on the parameters of the network. In this paper, we will clarify some of the theoretical results on monotone neural networks with positive weights, issues that are sometimes misunderstood in the neural network literature. Furthermore, we will generalize some of the results obtained by Sill for the so-called MIN-MAX networks to the case of partially monotone problems. The method is illustrated in practical case studies.