Inducing decision trees via concept lattices

We present a novel method for the construction of decision trees. The method utilises concept lattices in that certain formal concepts of the concept lattice associated to input data are used as nodes of the decision tree constructed from the data. The concept lattice provides global information about natural clusters in the input data, which we use for selection of splitting attributes. The usage of such global information is the main novelty of our approach. Experimental evaluation indicates good performance of our method. We describe the method, experimental results, and a comparison with standard methods on benchmark datasets.

[1]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[2]  Peter Øhrstrøm,et al.  Working with Conceptual Structures - Contributions to ICCS 2000 , 2000 .

[3]  Engelbert Mephu Nguifo,et al.  A Comparative Study of FCA-Based Supervised Classification Algorithms , 2004, ICFCA.

[4]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[5]  K MurthySreerama.  Automatic Construction of Decision Trees from Data , 1998 .

[6]  Claudio Carpineto,et al.  Concept data analysis - theory and applications , 2004 .

[7]  John Mingers,et al.  Expert Systems—Rule Induction with Statistical Data , 1987 .

[8]  Hemerson Pistori,et al.  Decision Tree Induction using Adaptive FSA , 2018, CLEI Electron. J..

[9]  L. Beran,et al.  [Formal concept analysis]. , 1996, Casopis lekaru ceskych.

[10]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[11]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[12]  Bernard De Baets,et al.  Trees in Concept Lattices , 2007, MDAI.

[13]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[14]  Radim Belohlávek,et al.  Formal Concept Analysis Constrained by Attribute-Dependency Formulas , 2005, ICFCA.

[15]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[16]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[17]  Ian Witten,et al.  Data Mining , 2000 .

[18]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..

[19]  Sergei O. Kuznetsov,et al.  Machine Learning and Formal Concept Analysis , 2004, ICFCA.

[20]  J. Ross Quinlan,et al.  Learning decision tree classifiers , 1996, CSUR.

[21]  Christian Lindig.  Fast Concept Analysis , 2000 .

[22]  Sreerama K. Murthy,et al.  Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey , 1998, Data Mining and Knowledge Discovery.

[23]  Engelbert Mephu Nguifo,et al.  IGLUE: A lattice-based constructive induction system , 2001, Intell. Data Anal..

[24]  D. J. Newman,et al.  UCI Repository of Machine Learning Database , 1998 .

[25]  Claudio Carpineto,et al.  A lattice conceptual clustering system and its application to browsing retrieval , 2004, Machine Learning.

[26]  Qiang Yang,et al.  Decision trees with minimal costs , 2004, ICML.

[27]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.