Evaluation of decision trees: a multi-criteria approach
Computers & Operations ResearchPublished 2 June 2003
Kweku-Muata Osei-Bryson
Citations145
SJR quartileQ1
SJR score1.60
SNIP2.02
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A multi-criteria decision analysis based process that would empower DM project teams to do thorough experimentation and analysis without being overwhelmed by the task of analyzing a significant number of DTs would offer a positive contribution to the DM process.
Abstract
Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classification of future events. The induction of the decision tree is done using a supervised knowledge discovery process in which prior knowledge regarding classes in the database is used to guide the discovery. The generation of a DT is a relatively easy task but in order to select the most appropriate DT it is necessary for the DM project team to generate and analyze a significant number of DTs based on multiple performance measures. We propose a multi-criteria decision analysis based process that would empower DM project teams to do thorough experimentation and analysis without being overwhelmed by the task of analyzing a significant number of DTs would offer a positive contribution to the DM process. We also offer some new approaches for measuring some of the performance criteria.
Keywords
Computer Science
Choice Reviews OnlineData mining: concepts and techniques
28,877 Citations2012Jiawei Han, Micheline Kamber +1 more
Data mining is the search for new, valuable, and nontrivial information in large volumes of data, a cooperative effort of humans and computers that is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining which produces new, nontrivials information based on the available data set.
Pattern RecognitionThe use of the area under the ROC curve in the evaluation of machine learning algorithms
7,233 Citations1997Andrew P. Bradley
AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities.
Machine LearningA Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms
1,132 Citations2000Tjen-Sien Lim, Wei‐Yin Loh +1 more
Among decision tree algorithms with univariate splits, C4.5, IND-CART, and QUEST have the best combinations of error rate and speed, but C 4.5 tends to produce trees with twice as many leaves as those fromIND-Cart and QUEST.
The Case against Accuracy Estimation for Comparing Induction Algorithms
1,024 Citations1998Foster Provost, Tom Fawcett +1 more
This work describes and demonstrates what it believes to be the proper use of ROC analysis for comparative studies in machine learning research, and argues that this methodology is preferable both for making practical choices and for drawing conclusions.
IEEE Transactions on Pattern Analysis and Machine IntelligenceA comparative analysis of methods for pruning decision trees
545 Citations1997Floriana Esposito, Donato Malerba +2 more
A comparative study of six well-known pruning methods with the aim of understanding their theoretical foundations, their computational complexity, and the strengths and weaknesses of their formulation, and an objective evaluation of the tendency to overprune/underprune observed in each method is made.
Industrial Management & Data SystemsMastering Data Mining: The Art and Science of Customer Relationship Management
475 Citations2000Michael A. Berry
The focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management is shifted, placing particular emphasis on customer relationship management.
Journal of the Operational Research SocietyA Goal Programming Method for Generating Priority Vectors
207 Citations1995Noel Bryson
A goal programming method is presented that has, as its objective, the generation of the priority vector whose associated comparison values are, on average, the closest to the pairwise comparison information provided by the evaluator.
Machine LearningTrading Accuracy for Simplicity in Decision Trees
141 Citations1994Marko Bohanec, Ivan Bratko
This paper addresses the problem: given a completely accurate, but complex, definition of a concept, simplify the definition, possibly at the expense of accuracy, so that the simplified definition still corresponds to the concept “sufficiently” well.
Machine LearningTrading accuracy for simplicity in decision trees
82 Citations1994Marko Bohanec, Ivan Bratko
This paper addresses the problem: given a completely accurate, but complex, definition of a concept, simplify the definition, possibly at the expense of accuracy, so that the simplified definition still corresponds to the concept “sufficiently” well.
OmegaTheory and practice of decision tree induction
69 Citations1995H Kim
Theoretical results of pruning are summarized and conditions under which pruning is necessary for better prediction accuracy are summarized.
ACM SIGKDD Explorations NewsletterMeasuring lift quality in database marketing
45 Citations2000Gregory Piatetsky-Shapiro, Sam Steingold
This paper proposes a measure, called L-quality, which indicates how close the lift curve is to the random and perfect models, and examines the practical issues of computing L- quality from discrete quantiles available in a typical lift table.
European Journal of Operational ResearchModelling pairwise comparisons on ratio scales
39 Citations1995Noel Bryson, Ayodele Mobolurin +1 more
This model provides a mathematical explanation for the phenomena of intransitivity and inconsistency that sometimes appear in situations involving scoring on a subjective/qualitative domain and develops methods for generating consistent intervals on ratio scales.
Efficient algorithms for constructing decision trees with constraints
36 Citations2000Minos Garofalakis, Dong-Joon Hyun +2 more
Novel branch-and-bound algorithms for pushing the constraints into the building phase of classi ers, and pruning early tree nodes that cannot possibly satisfy the constraints are developed.
Journal of Multi-Criteria Decision AnalysisGenerating consensus priority interval vectors for group decision-making in the AHP
34 Citations2000Noel Bryson, Anito Joseph
A set of techniques that could be used at some of the phases of an analytic hierarchy process (AHP)-based group decision-making process, which has the objective of generating a ‘consensus’ priority that represents the group's opinion with regards to the relative importance of a set of N objects, is presented.
Predictive modeling in automotive direct marketing
34 Citations2000Wendy Gersten, Rüdiger Wirth +1 more
This paper focuses on a particular project environment and describes tools which address issues across the whole data mining process, including a Quick Reference Guide for the standardization of the process and a library of re-usable procedures in the commercial data mining tool Clementine.