Feature selection in unsupervised learning via evolutionary search

By YongSeog Kim, W. Street, Filippo Menczer. Knowledge Discovery and Data Mining, 2000

339 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/031253e71c99fcc8229bbdb55272e91f8f673ef7

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. Lei Yu
3. Zheng Zhao
4. Anil K. Jain
5. Manoranjan Dash
6. Sonajharia Minz
7. Vipin Kumar
8. Lance Parsons

In Information Systems

1. Huan Liu
2. Zheng Zhao
3. Lei Yu
4. Sonajharia Minz
5. Vipin Kumar
6. Manoranjan Dash
7. Anil K. Jain
8. Lance Parsons

Citations in the corpus

• Image retrieval: Ideas, influences, and trends of the new age (2008, neutral): “Some of the other recent, more generic feature selection proposals involve boosting [Tieu and Viola 2004], evolutionary searching [Kim et al. 2000], Bayes classi.cation error [Carneiro and Vasconcelos 2005], and feature dependency/similarity measures [Mitra et al. 2002].”
• Subspace clustering for high dimensional data: a review (2004, methodology): “The wrapper method proposed in [47] forms a new feature subset and evaluates the resulting set by applying a standard k -means algorithm.”
• A critical review of multi-objective optimization in data mining: a position paper (2004, methodology): “…reporting not only the predictive accuracy of the model built from the selected attributes, but also the number of selected attributes and/or the size of the classification model built from the selected attributes [Yu & Liu 2003], [Kim et al. 2000], [Kohavi & John 1998], [Cherkauer & Shavlik 1996].”
• Toward integrating feature selection algorithms for classification and clustering (2005, methodology): “on developing dependent criteria in feature selection for clustering can been found in [20, 27, 42 ].”
• Simultaneous feature selection and clustering using mixture models (2004, methodology): “A genetic algorithm is used in [31] for feature selection in k-means clustering.”
• Feature Selection: A literature Review (2014, background): “W ra p p er Predictive Accuracy or Cluster Goodness BS [7], AMB&B [64], FSLC [65], FSBC [66], CARDIE [50], OBLIVIIN [63], SBS-SLASH [17], WSFG [28], WSBG [28], BDS [7], PQSS [7], RC [68], SS [69], Queiros’ [17], BSE [17], K2AS [71], RACE [72], SBS-W [28], SBS-SLASH [17] AICC [73], FSSEM [74], ELSA [”
• Feature selection for clustering - a filter solution (2002, methodology): “For example in [8, 17] theK-means clustering algorithm is used for evaluation, while in [11] EM – Expectation Maximization – is used for evaluation.”
• Content-based image retrieval: approaches and trends of the new age (2005, neutral): “Some of the other recent, more generic feature selection propositions involve boosting [94], evolutionary searching [54], Bayes”
• Semi-supervised Feature Selection via Spectral Analysis (2007, methodology): “Supervised feature selection methods [8] can be broadly categorized into wrapper models [19, 18] and filter models [15, 26].”
• Discovering expressive process models by clustering log traces (2006, background): “, [27], [28], [29], [30], [31]); as an instance, in [30], the problem of classifying sequences is dealt with by considering frequent subsequences of them as relevant features.”
• A Graphbased Framework for Multi-Task Multi-View Learning (2011, background): “In particularly, between Steps (9) and (17), for the k view, we collectively update the functions fik for the tasks with this view.”
• Efficient Feature Selection via Analysis of Relevance and Redundancy (2004, methodology): “Different feature selection methods can be broadly categorized into the wrapper model (Kohavi and John, 1997; Kim et al., 2000) and the filter model (Liu and Setiono, 1996; Liu et al., 2002b; Hall, 2000; Yu and Liu, 2003).”