A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms

By Dietrich Wettschereck, David William Aha, Takao Mohri. 1997

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Citations in the corpus

A Survey of Methods for Explaining Black Box Models (2018, background): “Indeed, rule-based classifiers have the disadvantage of requiring an additional approach for resolving such situations of conflicting outcome [133].”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “It can be considered a special case of feature weighting, as discussed in [23].”
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, background): “This is important when modeling hydrological processes, such as floods and stream flow, for which there is little or no prior knowledge of the data distribution [107].”
A Survey of Modern Questions and Challenges in Feature Extraction (2015, background): “Variable selection can be viewed as a special case of feature weighting (Wettschereck et al., 1997) when weights restricted to {0, 1}.”
Data Classification: Algorithms and Applications (2014, methodology): “An overview of instance-based methods may be found in [15, 16, 89].”
Machine learning: a review of classification and combining techniques (2006, methodology): “In our study, we also used the 3-NN algorithm, which combines robustness to noise and less time for classification than using a larger k for kNN (Wettschereck et al. 1997).”
A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions (2013, unknown): “[62].”
Hybrid case-based reasoning system by cost-sensitive neural network for classification (2016, background): “(1) and (2) could be found in Wettschereck et al. (1997).”
Reduction Techniques for Instance-Based Learning Algorithms (2000, methodology): “A good survey of weighting schemes is given by Wettschereck, Aha and Mohri (1997) .”
Character Recognition Systems: A Guide for Students and Practitioners (2007, background): “Typical examples of sequential search include sequential forward selection, sequential backward elimination [2, 56, 62], and branch and bound [83, 57, 81, 96]; whereas random mutation hill climbing [28, 87, 101], genetic algorithms [62, 96, 123, 115, 118, 120], simulated annealing [80], and tabu sea”
Comprehensible classification models: a position paper (2014, background): “A second approach to improve the comprehensibility of nearest neighbor predictions involves computing attribute weights [71], where the weight of an attribute is proportional to its predictive power.”
Case-based reasoning and neural network based expert system for personalization (2007, methodology): “Though many feature-weighted variants of k-nn have been reported to improve its retrieval accuracy on some tasks (Hastie & Tibshirani, 1996; Wettschereck & Aha, 1995; Wettschereck, Aha, & Mohri, 1997 ), few have been used in conjunction with the neural network learning until Shin et al. proposed”