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.

Learning optimal decision trees using constraint programming

Constraints ◽  
2020 ◽  
Vol 25 (3-4) ◽  
pp. 226-250
Author(s):  
Hélène Verhaeghe ◽  
Siegfried Nijssen ◽  
Gilles Pesant ◽  
Claude-Guy Quimper ◽  
Pierre Schaus
Keyword(s):  
Decision Trees ◽  
Constraint Programming ◽  
Optimal Decision

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.

A Building Block Approach to Genetic Programming for Rule Discovery

Data Mining ◽  
2011 ◽  
pp. 174-190 ◽  
Author(s):  
Andries P. Engelbrecht ◽  
L. Schoeman ◽  
Sonja Rouwhorst
Keyword(s):  
Decision Tree ◽  
Genetic Programming ◽  
Decision Trees ◽  
Building Block ◽  
Evolutionary Process ◽  
Building Blocks ◽  
Experimental Results ◽  
Rule Discovery ◽  
New Approach ◽  
Block Approach

Genetic programming has recently been used successfully to extract knowledge in the form of IF-THEN rules. For these genetic programming approaches to knowledge extraction from data, individuals represent decision trees. The main objective of the evolutionary process is therefore to evolve the best decision tree, or classifier, to describe the data. Rules are then extracted, after convergence, from the best individual. The current genetic programming approaches to evolve decision trees are computationally complex, since individuals are initialized to complete decision trees. This chapter discusses a new approach to genetic programming for rule extraction, namely the building block approach. This approach starts with individuals consisting of only one building block, and adds new building blocks during the evolutionary process when the simplicity of the individuals cannot account for the complexity in the underlying data. Experimental results are presented and compared with that of C4.5 and CN2. The chapter shows that the building block approach achieves very good accuracies compared to that of C4.5 and CN2. It is also shown that the building block approach extracts substantially less rules.

Factors Affecting Molecular Self-Assembly and Its Mechanism

2012 ◽  
Vol 9 (1) ◽  
pp. 43 ◽  
Author(s):  
Hueyling Tan
Keyword(s):  
Self Assembly ◽  
Building Blocks ◽  
Molecular Engineering ◽  
Molecular Structures ◽  
Polymer Science ◽  
New Approach ◽  
Factors Affecting ◽  
Dna Structures ◽  
Self Assembling ◽  
Wide Range

Molecular self-assembly is ubiquitous in nature and has emerged as a new approach to produce new materials in chemistry, engineering, nanotechnology, polymer science and materials. Molecular self-assembly has been attracting increasing interest from the scientific community in recent years due to its importance in understanding biology and a variety of diseases at the molecular level. In the last few years, considerable advances have been made in the use ofpeptides as building blocks to produce biological materials for wide range of applications, including fabricating novel supra-molecular structures and scaffolding for tissue repair. The study ofbiological self-assembly systems represents a significant advancement in molecular engineering and is a rapidly growing scientific and engineering field that crosses the boundaries ofexisting disciplines. Many self-assembling systems are rangefrom bi- andtri-block copolymers to DNA structures as well as simple and complex proteins andpeptides. The ultimate goal is to harness molecular self-assembly such that design andcontrol ofbottom-up processes is achieved thereby enabling exploitation of structures developed at the meso- and macro-scopic scale for the purposes oflife and non-life science applications. Such aspirations can be achievedthrough understanding thefundamental principles behind the selforganisation and self-synthesis processes exhibited by biological systems.

scholarly journals Compiling Life-Oriented Moral Education Textbooks for Elementary Schools in China: The Mimetic Approach in Morality and Law

2021 ◽  
pp. 209653112098296
Author(s):  
Yan Tang
Keyword(s):  
Elementary Schools ◽  
Moral Education ◽  
Life Events ◽  
Theoretical Perspective ◽  
Building Blocks ◽  
Learning Activities ◽  
Design Approach ◽  
New Approach ◽  
Novel Approach

Purpose: This study explores a novel approach to compiling life-oriented moral textbooks for elementary schools in China, specifically focusing on Morality and Law. Design/Approach/Methods: Adopting Aristotle’s Poetics as its theoretical perspective, this study illustrates and analyzes the mimetic approach used in compiling the life-oriented moral education textbook, Morality and Law. Findings: The mimetic approach involves imitating children's real activities, thoughts, and feelings in textbooks. The mimetic approach to compiling life-oriented moral textbooks comprises three strategies: constructing children's life events as building blocks for textbook compilation, designing an intricate textual device exposing the wholeness of children's life actions, and designing inward learning activities leading to children's inner worlds. Originality/Value: From the perspective of Aristotle's Poetics, the approach to compilation in Morality and Law can be defined as mimetic. And the compilation activity in the life-oriented moral education textbook also can be described as a processes of mimesis. So this article presents a new approach to compile moral education textbooks, and  an innovative way to understand the nature of one compiling activity.

scholarly journals Constraint programming approach to a bilevel scheduling problem

Constraints ◽  
2010 ◽  
Vol 16 (3) ◽  
pp. 317-340 ◽  
Author(s):  
András Kovács ◽  
Tamás Kis
Keyword(s):  
Constraint Programming ◽  
Programming Approach ◽  
Scheduling Problem
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