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Classification trees for problems with monotonicity constraints
2002
SIGKDD Explorations
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These are called classification problems with monotonicity constraints. ...
For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. ...
doi:10.1145/568574.568577
fatcat:3nmjoeos2jfpfkq6wgmgfzgdy4
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. ...
Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. ...
We sincerely thank the subject matter experts from Turvo Inc who provided us with valuable inputs and members of Starling Lab at UT Dallas for discussions and insights. ...
doi:10.1609/aaai.v34i04.5873
fatcat:c35usk3w6nbqnlremgjtev4654
Monotonic classification: an overview on algorithms, performance measures and data sets
[article]
2018
arXiv
pre-print
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For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. ...
In this way, this paper serves as an overview of the research about monotonic classification in specialized literature and can be used as a functional guide of the field. ...
Usually, in real monotonic classification problems, the monotonicity constraints are assumed only for a subset of the input features. ...
arXiv:1811.07155v1
fatcat:h7uqontgl5gdtgr3bduu73wg44
Monotonicity in Ant Colony Classification Algorithms
[chapter]
2016
Lecture Notes in Computer Science
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This paper proposes an extension to an existing ACO-based classification rule learner to create lists of monotonic classification rules. ...
Domain knowledge can be integrated into algorithms using semantic constraints to guide model construction. ...
algorithm is able to create monotonic classification rules with good predictive accuracy We further analysed the results of OLM and cAnt-Miner PB+MC -both algorithms that enforce monotonic constraints-for ...
doi:10.1007/978-3-319-44427-7_12
fatcat:ksbyp4lctvdynjm3i6fifldzb4
Comprehensible classification models
2014
SIGKDD Explorations
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constraints to improve the comprehensibility and acceptance of classification models by users. ...
trees, classification rules, decision tables, nearest neighbors and Bayesian network classifiers. ...
For researchers who need benchmarking datasets for evaluating classification algorithms that respect monotonicity constraints, information on monotonic constraints for five UCI datasets is given in [2 ...
doi:10.1145/2594473.2594475
fatcat:4w5frwv2zzd6thoevypzjdgtra
Isotonic Classification Trees
[chapter]
2009
Lecture Notes in Computer Science
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We propose a new algorithm for learning isotonic classification trees. It relabels non-monotone leaf nodes by performing the isotonic regression on the collection of leaf nodes. ...
We experimentally compare the performance of the new algorithm with standard classification trees. ...
In this paper we present a new algorithm, called ICT, for learning monotone classification trees for problems with ordered class labels. ...
doi:10.1007/978-3-642-03915-7_35
fatcat:qe2p5r4djjf23ce7gebhplbow4
Large-margin feature selection for monotonic classification
2012
Knowledge-Based Systems
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By introducing the monotonicity constraint into existing margin based feature selection algorithms, we design two new evaluation algorithms for monotonic classification. ...
Monotonic classification plays an important role in the field of decision analysis, where decision values are ordered and the samples with better feature values should not be classified into a worse class ...
In 2002, Potharst et al. gave an extensive review on classification trees for problems with monotonicity constraints [38] . ...
doi:10.1016/j.knosys.2012.01.011
fatcat:ojfnqnvfzzbhphwbdlbf4f3lz4
Rank Entropy-Based Decision Trees for Monotonic Classification
2012
IEEE Transactions on Knowledge and Data Engineering
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Such problems are called ordinal classification with monotonicity constraint. Some learning algorithms have been developed to handle this kind of tasks in recent years. ...
Moreover, there is a monotonic constraint that the objects with better feature values should not be assigned to a worse decision class. ...
ACKNOWLEDGMENTS The authors would like to express their gratitude to the anonymous reviewers for their constructive comments, which is helpful for improving the manuscript. ...
doi:10.1109/tkde.2011.149
fatcat:gx7hpppzmzfj3bgh5tgm4hta2q
Fusing Monotonic Decision Trees
2015
IEEE Transactions on Knowledge and Data Engineering
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Ordinal classification with a monotonicity constraint is a kind of classification tasks, in which the objects with better attribute values should not be assigned to a worse decision class. ...
The experimental analysis shows that the proposed fusing method can significantly improve classification performance of the learning system constructed by monotonic decision trees. ...
[21] introduced a fuzzy preference into rough sets for monotonic classification with a fuzzy consistent constraint. ...
doi:10.1109/tkde.2015.2429133
fatcat:am3tnd2w7bbprjezi3xvb32vny
Prediction Rule Reshaping
[article]
2018
arXiv
pre-print
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Two methods are proposed for high-dimensional shape-constrained regression and classification. ...
These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. ...
along with useful measures of uncertainty. ...
arXiv:1805.06439v1
fatcat:2tmzhjgsnzh6vgvhvojvldnoeu
Pruning for Monotone Classification Trees
[chapter]
2003
Lecture Notes in Computer Science
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These are called classification problems with monotonicity constraints. ...
For classification problems with ordinal attributes very often the class attribute should increase with each or some of the explanatory attributes. ...
Acknowledgements The authors would like to thank Paul Pompe for the kind donation of the bankruptcy data set, and Eelko Penninkx for helpful suggestions on earlier drafts of this paper. ...
doi:10.1007/978-3-540-45231-7_1
fatcat:mkiibzbrvjhfllvhcp6kfds7lm
Monotone Relabeling in Ordinal Classification
2010
2010 IEEE International Conference on Data Mining
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Moreover, we show how the algorithm can be extended to compute all optimal monotone classifications with little additional effort. Monotone relabeling is useful for at least two reasons. ...
We apply the new algorithm to investigate the effect on the prediction error of relabeling the training sample for k nearest neighbour classification and classification trees. ...
[10] consider ordinal classification with monotonicity constraints in the context of rough sets. ...
doi:10.1109/icdm.2010.92
dblp:conf/icdm/Feelders10
fatcat:wo2yhsjeyrar7c6rlqphiibcga
A better method to enforce monotonic constraints in regression and classification trees
[article]
2020
arXiv
pre-print
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In this report we present two new ways of enforcing monotone constraints in regression and classification trees. ...
With small or average trees, the loss reduction can be as high as 1% in the early stages of training and decreases to around 0.1% at the loss peak for the Adult dataset. ...
Let's imagine that we have a regression problem, with the data being represented on figure 11a. The only constraint is that the output has to be monotonically increasing horizontally. ...
arXiv:2011.00986v1
fatcat:6iwgxrcnfzc2zgtrfuzotiz7m4
Ranking and classification of monotonic emphysema patterns with a multi-class hierarchical approach
2014
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
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We exploit three solutions to this monotonic multi-class classification problem: a global rankSVM for ranking, hierarchical SVM for classification and a combination of these two, which we call a hierarchical ...
The classification accuracies were slightly better for hierarchical SVM. ...
For our problem with five classes, there are 14 possible trees. ...
doi:10.1109/isbi.2014.6868049
pmid:25485040
pmcid:PMC4254822
fatcat:rozl3t4czzc6xcq672jnpwc5lu
Prior Knowledge in Economic Applications of Data Mining
[chapter]
2000
Lecture Notes in Computer Science
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We explore the use of monotonicity constraints in classification tree algorithms. We present an application of monotonic classification trees to a problem in house pricing. ...
In this preliminary study we found that the monotonic trees were only slightly worse in classification performance, but were much simpler than their non-monotonic counterparts. ...
We have investigated the use of monotonicity constraints in classification tree algorithms. ...
doi:10.1007/3-540-45372-5_42
fatcat:f2t3antuibe5nhbjov7enqqgfi
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