The Strength of Weak Learnability
Machine Learning
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Original Contribution: Stacked generalization
Neural Networks
Boosting a weak learning algorithm by majority
Information and Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information, Prediction, and Query by Committee
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Learning and Evolution by Minimization of Mutual Information
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Applying Boosting Techniques to Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic Programming and Evolvable Machines
Boosting Kernel Models for Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Feature construction and dimension reduction using genetic programming
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Knowledge mining with genetic programming methods for variable selection in flavor design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary optimization of flavors
Proceedings of the 12th annual conference on Genetic and evolutionary computation
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
Coevolution of Fitness Predictors
IEEE Transactions on Evolutionary Computation
Knowledge discovery through symbolic regression with heuristiclab
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors' ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.