A New Approach Based on Bat Algorithm for Inducing Optimal Decision Trees Classifiers

2018 ◽  
pp. 631-640 ◽  
Author(s):  
Ikram Bida ◽  
Saliha Aouat
Keyword(s):  
Decision Trees ◽  
Bat Algorithm ◽  
Optimal Decision ◽  
New Approach

scholarly journals Learning Optimal Decision Trees using Constraint Programming (Extended Abstract)

2020 ◽  
Author(s):  
Hélène Verhaeghe ◽  
Siegfried Nijssen ◽  
Gilles Pesant ◽  
Claude-Guy Quimper ◽  
Pierre Schaus
Keyword(s):  
Decision Trees ◽  
Constraint Programming ◽  
Greedy Algorithms ◽  
Building Blocks ◽  
Optimal Decision ◽  
Programming Approach ◽  
Learning Problem ◽  
New Approach ◽  
Good Classification ◽  
Additional Constraints

Decision trees are among the most popular classification models in machine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms have their disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there has been a recent interest in exact and flexible algorithms for learning decision trees. In this paper, we introduce a new approach to learn decision trees using constraint programming. Compared to earlier approaches, we show that our approach obtains better performance, while still being sufficiently flexible to allow for the inclusion of constraints. Our approach builds on three key building blocks: (1) the use of AND/OR search, (2) the use of caching, (3) the use of the CoverSize global constraint proposed recently for the problem of itemset mining. This allows our constraint programming approach to deal in a much more efficient way with the decompositions in the learning problem.

scholarly journals Learning Optimal Decision Trees Using Caching Branch-and-Bound Search

2020 ◽  
Vol 34 (04) ◽  
pp. 3146-3153
Author(s):  
Gaël Aglin ◽  
Siegfried Nijssen ◽  
Pierre Schaus
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Branch And Bound ◽  
Search Space ◽  
Mixed Integer ◽  
Optimal Decision ◽  
New Approach ◽  
Branch And Bound Search ◽  
New Type ◽  
Formal Requirements

Several recent publications have studied the use of Mixed Integer Programming (MIP) for finding an optimal decision tree, that is, the best decision tree under formal requirements on accuracy, fairness or interpretability of the predictive model. These publications used MIP to deal with the hard computational challenge of finding such trees. In this paper, we introduce a new efficient algorithm, DL8.5, for finding optimal decision trees, based on the use of itemset mining techniques. We show that this new approach outperforms earlier approaches with several orders of magnitude, for both numerical and discrete data, and is generic as well. The key idea underlying this new approach is the use of a cache of itemsets in combination with branch-and-bound search; this new type of cache also stores results for parts of the search space that have been traversed partially.

scholarly journals Optimal Decision Trees on Simplicial Complexes

10.37236/1900 ◽  
2005 ◽  
Vol 12 (1) ◽  
Author(s):  
Jakob Jonsson
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Simplicial Complex ◽  
Elementary Theory ◽  
Simplicial Complexes ◽  
Optimal Decision ◽  
Property A ◽  
Recursive Definition ◽  
Topological Combinatorics ◽  
Definition Of

We consider topological aspects of decision trees on simplicial complexes, concentrating on how to use decision trees as a tool in topological combinatorics. By Robin Forman's discrete Morse theory, the number of evasive faces of a given dimension $i$ with respect to a decision tree on a simplicial complex is greater than or equal to the $i$th reduced Betti number (over any field) of the complex. Under certain favorable circumstances, a simplicial complex admits an "optimal" decision tree such that equality holds for each $i$; we may hence read off the homology directly from the tree. We provide a recursive definition of the class of semi-nonevasive simplicial complexes with this property. A certain generalization turns out to yield the class of semi-collapsible simplicial complexes that admit an optimal discrete Morse function in the analogous sense. In addition, we develop some elementary theory about semi-nonevasive and semi-collapsible complexes. Finally, we provide explicit optimal decision trees for several well-known simplicial complexes.

scholarly journals PyDL8.5: a Library for Learning Optimal Decision Trees

2020 ◽  
Author(s):  
Gaël Aglin ◽  
Siegfried Nijssen ◽  
Pierre Schaus
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Efficient Algorithm ◽  
Optimal Decision ◽  
Learning Tasks ◽  
Explainable Ai ◽  
Classification Tasks ◽  
Interpretable Models ◽  
Limited Depth

Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.

Prediction of irrigation event occurrence at farm level using optimal decision trees

2019 ◽  
Vol 157 ◽  
pp. 173-180 ◽  
Author(s):  
R. González Perea ◽  
E. Camacho Poyato ◽  
P. Montesinos ◽  
J.A. Rodríguez Díaz
Keyword(s):  
Decision Trees ◽  
Irrigation Event ◽  
Optimal Decision ◽  
Farm Level ◽  
Event Occurrence

scholarly journals Experimental Study of Totally Optimal Decision Trees

2019 ◽  
Vol 165 (3-4) ◽  
pp. 245-261
Author(s):  
Abdulla Aldilaijan ◽  
Mohammad Azad ◽  
Mikhail Moshkov
Keyword(s):  
Experimental Study ◽  
Decision Trees ◽  
Optimal Decision
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