- author = "Vinicius Veloso {de Melo} and Wolfgang Banzhaf",
- title = "Kaizen Programming for Feature Generation",
- booktitle = "Genetic Programming Theory and Practice XIII",
- year = "2015",
- editor = "Rick Riolo and William P. Worzel and M. Kotanchek and A. Kordon",
- series = "Genetic and Evolutionary Computation",
- pages = "39--57",
- address = "Ann Arbor, USA",
- month = "14-16 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Kaizen programming Genetic programming Classification Decision-tree",
- isbn13 = "978-3-319-34223-8",
-
URL = "
http://www.springer.com/us/book/9783319342214",
-
DOI = "
doi:10.1007/978-3-319-34223-8_3",
- abstract = "A data set for classification is commonly composed of a set of features defining the data space representation and one attribute corresponding to the instances class. A classification tool has to discover how to separate classes based on features, but the discovery of useful knowledge may be hampered by inadequate or insufficient features. Pre-processing steps for the automatic construction of new high-level features proposed to discover hidden relationships among features and to improve classification quality. Here we present a new tool for high-level feature construction: Kaizen Programming. This tool can construct many complementary/dependent high-level features simultaneously. We show that our approach outperforms related methods on well-known binary-class medical data sets using a decision-tree classifier, achieving greater accuracy and smaller trees.",
-
notes = "
Part of \cite{Riolo:2015:GPTP} Published after the workshop in 2016",
