Learning and classification of monotonic ordinal concepts
Computational Intelligence
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Developing and Testing Models for Replicating Credit Ratings: A Multicriteria Approach
Computational Economics
A content-search information retrieval process based on conceptual graphs
Knowledge and Information Systems
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision trees for ordinal classification
Intelligent Data Analysis
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Knowledge and Information Systems
Nonparametric Monotone Classification with MOCA
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Feature selection based on loss-margin of nearest neighbor classification
Pattern Recognition
Set-valued ordered information systems
Information Sciences: an International Journal
Rule learning with monotonicity constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An optimization of ReliefF for classification in large datasets
Data & Knowledge Engineering
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Fuzzy preference based rough sets
Information Sciences: an International Journal
Ensemble of decision rules for ordinal classification with monotonicity constraints
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Attribute reduction in ordered information systems based on evidence theory
Knowledge and Information Systems
A rough set approach to feature selection based on power set tree
Knowledge-Based Systems
Fuzzy rough set based attribute reduction for information systems with fuzzy decisions
Knowledge-Based Systems
A two-grade approach to ranking interval data
Knowledge-Based Systems
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Feature Selection for Monotonic Classification
IEEE Transactions on Fuzzy Systems
Rank Entropy-Based Decision Trees for Monotonic Classification
IEEE Transactions on Knowledge and Data Engineering
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
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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. The monotonic classification tasks seem conceptually simple, but difficult to utilize and explain the order structure in practice. In this work, we discuss the issue of feature selection under the monotonicity constraint based on the principle of large margin. By introducing the monotonicity constraint into existing margin based feature selection algorithms, we design two new evaluation algorithms for monotonic classification. The proposed algorithms are tested with some artificial and real data sets, and the experimental results show its effectiveness.