Combining classifiers in text categorization

By Leah S. Larkey, W. Bruce Croft. 1996

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Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, methodology): “In text categorization, standard baseline classifiers include k-nearest neighbors (kNN) (Larkey and Croft, 1996), Naive Bayes (NB) (McCallum and Nigam, 1998) and linear Support Vector Machines (SVM) (Joachims, 1998) with the latter performing the best on n-gram features as verified in our experiment”
Text Classification from Labeled and Unlabeled Documents using EM (2000, methodology): “…Bayes text classification that instead uses a generative model and document representation in which each word in the vocabulary is a binary feature, and is modeled by a mixture of multi-variate Bernoullis (Robertson & Sparck-Jones, 1976; Lewis, 1992; Larkey & Croft, 1996; Koller & Sahami , 1997).”
TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY (2012, methodology): “[43] [44] has used combinations of different classifiers with different functions.”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “Probabilistic classifiers have gained a lot of popularity recently and have shown to perform remarkably well [24, 73, 84, 86, 118].”
Ad hoc classification of radiology reports. (1999, methodology): “used more intensive statistical analysis, the nearest-neighbor approach, to categorize Medline articles and surgical procedures. Larkey and Croft”
Simlarity Search for Web Services (2004, background): “Most solutions to this problem (e.g. [10] [20] [27] [19]) are based on term frequency analysis.”
Mining the Biomedical Literature in the Genomic Era: An Overview (2003, unknown): “The other approach is based on machine learning (Cohen and Singer, 1999; Dumais et al. , 1998; Joachims, 1998; Larkey and Croft, 1996; Lewis, 1995; Lewis and Hayes, 1994; Lewis and Ringuette, 1994; Lewis et al. , 1996; Riloff and Lehnert, 1994; Sebastiani, 2002; Vapnik, 1995; Yang and Chute, 1994;…”
Ensemble of feature sets and classification algorithms for sentiment classification (2011, unknown): “In the early work [18], a combination of different classification algorithms (k-NN, Relevance feedback and Bayesian classifier) produces better results than any single type of classifier.”
A comparison of event models for naive bayes text classification (1998, methodology): “The approach has been used for text classification by numerous people (Robertson and Sparck-Jones 1976; Lewis 1992; Kalt and Croft 1996; Larkey and Croft 1996; Koller and Sahami 1997; Sahami 1996).”
A Survey of Text Classification Algorithms (2012, methodology): “For example, the work by Larkey and Croft [79] used weighted linear combinations of the classifier scores or ranks.”
Mining Text Data (2012, methodology): “For example, the work by Larkey and Croft [79] used weighted linear combinations of the classifier scores or ranks.”