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AAAI
2008
13 years 7 months ago
Trace Ratio Criterion for Feature Selection
Fisher score and Laplacian score are two popular feature selection algorithms, both of which belong to the general graph-based feature selection framework. In this framework, a fe...
Feiping Nie, Shiming Xiang, Yangqing Jia, Changshu...
AAAI
2008
13 years 7 months ago
Concept-Based Feature Generation and Selection for Information Retrieval
Traditional information retrieval systems use query words to identify relevant documents. In difficult retrieval tasks, however, one needs access to a wealth of background knowled...
Ofer Egozi, Evgeniy Gabrilovich, Shaul Markovitch
ICML
1997
IEEE
13 years 8 months ago
Efficient Feature Selection in Conceptual Clustering
Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We i...
Mark Devaney, Ashwin Ram
RSCTC
2000
Springer
151views Fuzzy Logic» more  RSCTC 2000»
13 years 8 months ago
Anytime Algorithm for Feature Selection
Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally ...
Mark Last, Abraham Kandel, Oded Maimon, Eugene Ebe...
PAKDD
2000
ACM
124views Data Mining» more  PAKDD 2000»
13 years 8 months ago
Feature Selection for Clustering
In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individu...
Manoranjan Dash, Huan Liu
EWCBR
2006
Springer
13 years 8 months ago
Unsupervised Feature Selection for Text Data
Feature selection for unsupervised tasks is particularly challenging, especially when dealing with text data. The increase in online documents and email communication creates a nee...
Nirmalie Wiratunga, Robert Lothian, Stewart Massie
EVOW
2006
Springer
13 years 8 months ago
Robust SVM-Based Biomarker Selection with Noisy Mass Spectrometric Proteomic Data
Abstract. Computational analysis of mass spectrometric (MS) proteomic data from sera is of potential relevance for diagnosis, prognosis, choice of therapy, and study of disease act...
Elena Marchiori, Connie R. Jimenez, Mikkel West-Ni...
CIKM
2006
Springer
13 years 8 months ago
Coupling feature selection and machine learning methods for navigational query identification
It is important yet hard to identify navigational queries in Web search due to a lack of sufficient information in Web queries, which are typically very short. In this paper we st...
Yumao Lu, Fuchun Peng, Xin Li, Nawaaz Ahmed
CIARP
2006
Springer
13 years 8 months ago
Feature Selection Based on Mutual Correlation
Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filte...
Michal Haindl, Petr Somol, Dimitrios Ververidis, C...
KDD
2010
ACM
310views Data Mining» more  KDD 2010»
13 years 8 months ago
An integrated machine learning approach to stroke prediction
Stroke is the third leading cause of death and the principal cause of serious long-term disability in the United States. Accurate prediction of stroke is highly valuable for early...
Aditya Khosla, Yu Cao, Cliff Chiung-Yu Lin, Hsu-Ku...