A fast heuristic attribute reduction approach to ordered decision systems
Section snippets
Introduction and related work
Feature (subset) selection, also known as attribute reduction by the rough set community, becomes naturally an important but difficult problem encountered in many practical areas such as machine learning, pattern recognition and data mining, especially in this era of information explosion (Blum, Langley, 1997, Gunal, Edizkan, 2008, Piramuthu, 2004). The main objective of this technique is to seek the relevant features from the original feature set without incurring much loss of information.
Dominance-based rough set approach
In this section, to make this paper self-contained, we give a brief introduction to several basic topics, such as lower and upper approximations, and the quality of classification of ordered information systems.
Two common algorithms for acquisition of a reduct of an ordered decision system
Given an information system, a reduct is a minimal subset which has the same discriminating or sorting ability as the full set of available attributes in the system. In this section, the DRSQR algorithm as well as the HARCC algorithm is provided to derive a (super-)reduct of a given ODS.
An accelerator for attribute reduction in ordered decision systems
Despite the DRSQR/HARNC algorithm and the HARCC algorithm are of polynomial time complexity with respect to |U| and |C|, one still needs a rather long time when they are applied to high dimensional data sets. To improve the performance of proposed algorithms, we attempt to find ways of reducing the number of objects and dimension of criteria.
Experiments and analysis
In this section, some real-world tasks are gathered in the empirical study whose objective is to test the feasibility and efficiency of the proposed methods.
Ordinal classification with monotonicity constraints (monotonicity classification for short) is a special class of ordinal regression problems. In this task, object x is described by a k-dimensional vector and is assigned the ordered class label λ(x). The monotonicity constraints can be expressed as (Kotłowski, Dembczyński,
Conclusions
Dominance-based rough set approach takes users’ preferences into consideration for reasoning about ordinal data, which distinguishes itself from other extensions of the rough set theory. The classical reduct which preserves the quality of classification is usually compared with other kinds of reducts in aspects such as length, stability, computational time and classification accuracy. Thus, an efficient acquisition scheme for a reduct is a necessity for further study from an empirical
Acknowledgments
The authors sincerely thank the three anonymous reviewers for their constructive comments and valuable suggestions which helped improve this paper significantly. This research was supported by the National Natural Science Foundation of China (Grant nos. 11571010, 61179038) and the Fundamental Research Funds for the Central Universities (Grant no. 2015201020201).
References (65)
- et al.
Selection of relevant features and examples in machine learning
Artificial Intelligence
(1997) - et al.
Dominance-based rough set approach for group decisions
European Journal of Operational Research
(2016) - et al.
Feature selection for classification
Intelligent Data Analysis
(1997) - et al.
Rough set approach to multiple criteria classification with imprecise evaluations and assignments
European Journal of Operational Research
(2009) - et al.
Approximate distribution reducts in inconsistent interval-valued ordered decision tables
Information Sciences
(2014) - et al.
Attribute reduction in ordered decision tables via evidence theory
Information Sciences
(2016) - et al.
Dominance-based rough set approach to incomplete ordered information systems
Information Sciences
(2016) - et al.
Rough set-based logics for multicriteria decision analysis
European Journal of Operational Research
(2007) - et al.
Rough approximation of a preference relation by dominance relations
European Journal of Operational Research
(1999) - et al.
Rough sets theory for multicriteria decision analysis
European Journal of Operational Research
(2001)
Rough sets methodology for sorting problems in presence of multiple attributes and criteria
European Journal of Operational Research
Putting Dominance-based Rough Set Approach and robust ordinal regression together
Decision Support Systems
Subspace based feature selection for pattern recognition
Information Sciences
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Robust Ordinal Regression for Dominance-based Rough Set Approach to multiple criteria sorting
Information Sciences
Wrappers for feature subset selection
Artificial Intelligence
Stochastic dominance-based rough set model for ordinal classification
Information Sciences
An accelerator for attribute reduction based on perspective of objects and attributes
Knowledge-Based Systems
Rough sets
International Journal of Computer and Information Sciences
Classification trees for problems with monotonicity constraints
ACM SIGKDD Explorations Newsletter
An efficient accelerator for attribute reduction from incomplete data in rough set framework
Pattern Recognition
Fuzzy-rough feature selection accelerator
Fuzzy Sets and Systems
Induction of decision trees
Machine Learning
A rough-fuzzy approach for generating classification rules
Pattern Recognition
A fast approach to attribute reduction from perspective of attribute measures in incomplete decision systems
Knowledge-Based Systems
An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory
Data and Knowledge Engineering
The discernibility matrices and functions in information systems
Generation of rough sets reducts and constructs based on inter-class and intra-class information
Fuzzy Sets and Systems
Generation of reducts and rules in multi-attribute and multi-criteria classification
Control and Cybernetics
Variable consistency dominance-based rough set approach to preference learning in multicriteria ranking
Information Sciences
The two sides of the theory of rough sets
Knowledge-Based Systems
Cited by (36)
Feature selection of dominance-based neighborhood rough set approach for processing hybrid ordered data
2024, International Journal of Approximate ReasoningFuzzy rough feature selection using a robust non-linear vague quantifier for ordinal classification
2023, Expert Systems with ApplicationsOptimal scale selection based on multi-scale single-valued neutrosophic decision-theoretic rough set with cost-sensitivity
2023, International Journal of Approximate ReasoningA novel incremental attribute reduction by using quantitative dominance-based neighborhood self-information
2023, Knowledge-Based SystemsCitation Excerpt :Therefore, attribute reduction methods based on DRSA and its extended models are also one of the mainstreams of current research [22–24]. Du and Hu introduced a QuickReduct method and a heuristic attribute reduction method based on the DRSA model [25]. To deal with numerical ordered data, Hu et al. explored an extended DRSA, namely the fuzzy preference based rough set model, and proposed the corresponding feature selection algorithm [26].
Self-adaptive weighted interaction feature selection based on robust fuzzy dominance rough sets for monotonic classification
2022, Knowledge-Based SystemsCitation Excerpt :Qian et al. introduced an attribute reduction method with rank-preservation based on the variable dominance rough set model [49]. Du et al. presented a heuristic attribute reduction method based on dominance-based rough fuzzy set model [21], and then they successively introduced QuickReduct algorithm and heuristic attribute reduction algorithm based on DRSA [50]. Based on FDRS model, Wang et al. designed an ensemble learning strategy based on the discernibility matrix for feature selection [30].
Matrix representation of the conditional entropy for incremental feature selection on multi-source data
2022, Information Sciences