Toward integrating feature selection algorithms for classification and clustering

By Huan Liu, Lei Yu. IEEE Transactions on Knowledge and Data Engineering, 2005

2,860 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/185e7e2d397478d0d81b89c6f712b1fa7d62c979

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huan Liu
2. Yi Yang
3. Mengjie Zhang
4. Jiliang Tang
5. Bing Xue
6. Tansel Dökeroğlu
7. Dacheng Tao
8. Xuelong Li

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Yi Yang
3. Luc Van Gool
4. Xuelong Li
5. Trevor Darrell
6. Xiaogang Wang
7. Shuicheng Yan
8. Ming-Hsuan Yang

In Information Systems

1. Huan Liu
2. Michael R. Lyu
3. Irwin King
4. Qinghua Hu
5. Daren Yu
6. Jiliang Tang
7. Jinfu Liu
8. Richard Jensen

Citations in the corpus

• A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data (2015, background): “These meth-ods can be categorized into two classes, namely, the supervised and unsupervised methods [17].”
• Feature selection for text classification: A review (2018, methodology): “Detailed investigation and comparison between different feature selection algorithms for generic data can be found in [20, 60, 66, 82].”
• Machine Learning and Radiology (2012, methodology): “Feature selection methods can be classified into two types – filter and wrapper – depending on the integration of feature selection and the problem to be solved (Liu and Yu, 2005).”
• Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “In this manner, embedded models have the advantages of both wrapper models, i.e., they include the interaction with the ML algorithm, and filter models, i.e., they are far less computationally intensive than wrapper methods [24].”
• An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, background): “For more FS approaches, readers can refer to the available review studies [64, 65].”
• Discriminative Semi-Supervised Feature Selection Via Manifold Regularization (2009, background): “Feature selection can be conducted in a supervised or unsupervised manner, in terms of whether the label information is utilized to guide the selection of relevant features [36].”
• Physical Human Activity Recognition Using Wearable Sensors (2015, methodology): “[47] categorized the feature selection process in a three- dimensional framework into a data mining task, an evaluation criterion, and a search strategy.”
• A Survey on Evolutionary Computation Approaches to Feature Selection (2016, methodology): “A variety of search techniques have been applied to feature selection, such as complete search, greedy search, heuristic search, and random search [1], [6]–[9].”
• Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences (2014, methodology): “Future work could 29:14 N. Li et al. build on this work by automating the feature-selection step in order to better address contextual dependencies [Guyon and Elisseeff 2003; Liu and Yu 2005].”
• Feature Selection: A literature Review (2014, methodology): “It is not advised to modify feature selection algorithms for sparse data [8].”
• A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification (2010, methodology): “Some of the stopping criteria described in [13] are 1.”