Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Demand-Driven Construction of Structural Features in ILP
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Feature Selection for Neural Networks through Functional Links Found by Evolutionary Computation
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Ranking a random feature for variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evolutionary Constructive Induction
IEEE Transactions on Knowledge and Data Engineering
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Feature discovery in classification problems
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Evolution of functional link networks
IEEE Transactions on Evolutionary Computation
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In data mining problems, the selection of appropriate input transformations is often crucial to obtain good solutions. The purpose of such transformations is to project the original attribute space onto a new one that, being closer to the problem structure, allows for more compact and interpretable solutions. We address the problem of automatic construction of input transformations in classification problems. We use an evolutionary approach to search the space of input transformations and a linear method to perform classification on the new feature space. Our assumption is that once a proper data representation, which captures the problem structure, is found, even a linear classifier may find a good solution. Linear methods are free from local minima, while the use of a representation space closer to the problem structure will in general provide more compact and interpretable solutions. We test our method using an artificial problem and a real classification problem from the UCI database. In both cases we obtain low error solutions that in addition are compact and interpretable.