MLC++

MLC++: a machine learning library in C++. We present MLC++, a library of C++ classes and tools for supervised machine learning. While MLC++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC++ is can attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC++ aims to solve, the design of MLC++, and the current functionality


References in zbMATH (referenced in 41 articles )

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  1. Hutter, Marcus; Zaffalon, Marco: Bayesian treatment of incomplete discrete data applied to mutual information and feature selection (2003)
  2. Pernkopf, Franz; O’Leary, Paul: Floating search algorithm for structure learning of Bayesian network classifiers. (2003)
  3. Ranilla, José; Luaces, Oscar; Bahamonde, Antonio: A heuristic for learning decision trees and pruning them into classification rules. (2003)
  4. Duffy, Nigel; Helmbold, David: A geometric approach to leveraging weak learners (2002)
  5. Fan, Hongjian; Kotagiri, Ramamohanarao: An efficient single-scan algorithm for mining essential jumping emerging patterns for classification (2002)
  6. Hilario, Melanie: Model complexity and algorithm selection in classification (2002)
  7. Valentini, Giorgio; Masulli, Francesco: NEURObjects: an object-oriented library for neural network development (2002)
  8. Amado, Nuno; Gama, João; Silva, Fernando: Parallel implementation of decision tree learning algorithms (2001)
  9. Brazdil, Pavel; Soares, Carlos; Pereira, Rui: Reducing rankings of classifiers by eliminating redundant classifiers (2001)
  10. Dhar, Vasant: A comparison of GLOWER and other machine learning methods for investment decision making (2001)
  11. Hilario, Melanie; Kalousis, Alexandros: Fusion of meta-knowledge and meta-data for case-based model selection (2001)
  12. Inza, Iñaki; Larrañaga, Pedro; Sierra, Basilio: Feature subset selection by Bayesian networks: A comparison with genetic and sequential algorithms (2001)
  13. Langdon, W. B.; Buxton, B. F.: Genetic programming for improved receiver operating characteristics (2001)
  14. Li, Jinyan; Dong, Guozhu; Ramamohanarao, Kotagiri: Making use of the most expressive jumping emerging patterns for classification (2001)
  15. Li, Jinyan; Ramamohanarao, Kotagiri; Dong, Guozhu: Combining the strength of pattern frequency and distance for classification (2001)
  16. Diamantidis, N. A.; Karlis, D.; Giakoumakis, E. A.: Unsupervised stratification of cross-validation for accuracy estimation (2000)
  17. Inza, I.; Larrañaga, P.; Etxeberria, R.; Sierra, B.: Feature Subset Selection by Bayesian network-based optimization (2000)
  18. Verykios, Vassilios S.; Elmagarmid, Ahmed K.; Houstis, Elias N.: Automating the approximate record-matching process (2000)
  19. Reinartz, Thomas: Focusing solutions for data mining. Analytical studies and experimental results in real-world domains. (1999)
  20. Kohavi, Ron; John, George H.: Wrappers for feature subset selection (1997)