Results 1  10
of
15
MetaCost: A General Method for Making Classifiers CostSensitive
 In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining
, 1999
"... Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob lems. Individually making each classification learner costsensi ..."
Abstract

Cited by 415 (4 self)
 Add to MetaCart
(Show Context)
Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob lems. Individually making each classification learner costsensitive is laborious, and often nontrivial. In this paper we propose a principled method for making an arbitrary classifier costsensitive by wrapping a costminimizing procedure around it. This procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. Unlike stratification, MetaCost is applicable to any number of classes and to arbitrary cost matrices. Empirical trials on a large suite of benchmark databases show that MetaCost almost always produces large cost reductions compared to the costblind classifier used (C4.5RULES) and to two forms of stratification. Further tests identify the key components of MetaCost and those that can be varied without substantial loss. Experiments on a larger database indicate that MetaCost scales well.
Ensemble Feature Selection with the Simple Bayesian Classification
"... A popular method for creating an accurate classifier from a set of training data is to build several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. One way to generate an ensemble of accurate and di ..."
Abstract

Cited by 41 (8 self)
 Add to MetaCart
A popular method for creating an accurate classifier from a set of training data is to build several classifiers, and then to combine their predictions. The ensembles of simple Bayesian classifiers have traditionally not been a focus of research. One way to generate an ensemble of accurate and diverse simple Bayesian classifiers is to use different feature subsets generated with the random subspace method. In this case, the ensemble consists of multiple classifiers constructed by randomly selecting feature subsets, that is, classifiers constructed in randomly chosen subspaces. In this paper, we present an algorithm for building ensembles of simple Bayesian classifiers in random subspaces...
Discretization for naiveBayes learning: managing discretization bias and variance
, 2003
"... Quantitative attributes are usually discretized in naiveBayes learning. We prove a theorem that explains why discretization can be effective for naiveBayes learning. The use of different discretization techniques can be expected to affect the classification bias and variance of generated naiveBay ..."
Abstract

Cited by 37 (8 self)
 Add to MetaCart
Quantitative attributes are usually discretized in naiveBayes learning. We prove a theorem that explains why discretization can be effective for naiveBayes learning. The use of different discretization techniques can be expected to affect the classification bias and variance of generated naiveBayes classifiers, effects we name discretization bias and variance. We argue that by properly managing discretization bias and variance, we can effectively reduce naiveBayes classification error. In particular, we propose proportional kinterval discretization and equal size discretization, two efficient heuristic discretization methods that are able to effectively manage discretization bias and variance by tuning discretized interval size and interval number. We empirically evaluate our new techniques against five key discretization methods for naiveBayes classifiers. The experimental results support our theoretical arguments by showing that naiveBayes classifiers trained on data discretized by our new methods are able to achieve lower classification error than those trained on data discretized by alternative discretization methods.
SNNB: A Selective Neighborhood based Naive Bayes for Lazy Learning
 IN: PROCEEDINGS OF THE 6TH PAKDD
, 2002
"... Naive Bayes is a probabilitybased classification method which is based on the assumption that attributes are conditionally mutually independent given the class label. Much research has been focused on improving the accuracy of Nave Bayes via eager learning. In this paper, we propose a novel laz ..."
Abstract

Cited by 16 (0 self)
 Add to MetaCart
Naive Bayes is a probabilitybased classification method which is based on the assumption that attributes are conditionally mutually independent given the class label. Much research has been focused on improving the accuracy of Nave Bayes via eager learning. In this paper, we propose a novel lazy learning algorithm, Selective Neighbourhood based Nave Bayes (SNNB).
Identifying table boundaries in digital documents via sparse line detection
 IN: PROCEEDINGS OF CIKM08
, 2008
"... Most prior work on information extraction has focused on extracting information from text in digital documents. However, often, the most important information being reported in an article is presented in tabular form in a digital document. If the data reported in tables can be extracted and stored i ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
(Show Context)
Most prior work on information extraction has focused on extracting information from text in digital documents. However, often, the most important information being reported in an article is presented in tabular form in a digital document. If the data reported in tables can be extracted and stored in a database, the data can be queried and joined with other data using database management systems. In order to prepare the data source for table search, accurately detecting the table boundary plays a crucial role for the later table structure decomposition. Table boundary detection and content extraction is a challenging problem because tabular formats are not standardized across all documents. In this paper, we propose a simple but effective preprocessing method to improve the table boundary detection performance by considering the sparseline property of table rows. Our method easily simplifies the table boundary detection problem into the sparse line analysis problem with much less noise. We design eight line label types and apply two machine learning techniques, Conditional Random Field (CRF) and Support Vector Machines (SVM), on the table boundary detection field. The experimental results not only compare the performances between the machine learning methods and the heuristicalbased method, but also demonstrate the effectiveness of the sparse line analysis in the table boundary detection.
Generating Classifier Committees by Stochastically Selecting both Attributes and Training Examples
 Proceedings 5th Pacific Rim International Conferences on Artificial Intelligence (PRICAI’98
, 1998
"... . Boosting and Bagging, as two representative approaches to learning classifier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build different classifiers using a base learning algorithm by changing the distribution of the training set. Sasc, as a ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
. Boosting and Bagging, as two representative approaches to learning classifier committees, have demonstrated great success, especially for decision tree learning. They repeatedly build different classifiers using a base learning algorithm by changing the distribution of the training set. Sasc, as a different type of committee learning method, can also significantly reduce the error rate of decision trees. It generates classifier committees by stochastically modifying the set of attributes but keeping the distribution of the training set unchanged. It has been shown that Bagging and Sasc are, on average, less accurate than Boosting, but the performance of the former is more stable than that of the latter in terms of less frequently obtaining significantly higher error rates than the base learning algorithm. In this paper, we propose a novel committee learning algorithm, called SascBag, that combines Sasc and Bagging. It creates different classifiers by stochastically varying both the a...
Context and Keyword Extraction in Plain Text using a Graph Representation
"... ar ..."
(Show Context)
Author manuscript, published in "IEEE International Conference on Signal Image Technology and Internet Based Systems, SITIS '08., Bali: Indonesia (2008)" Context and Keyword Extraction in Plain Text using a Graph Representation
, 2009
"... Document indexation is an essential task achieved by archivists or automatic indexing tools. To retrieve relevant documents to a query, keywords describing this document have to be carefully chosen. Archivists have to find out the right topic of a document before starting ..."
Abstract
 Add to MetaCart
(Show Context)
Document indexation is an essential task achieved by archivists or automatic indexing tools. To retrieve relevant documents to a query, keywords describing this document have to be carefully chosen. Archivists have to find out the right topic of a document before starting
unknown title
"... Discretization for naiveBayes learning: managing discretization bias and variance ..."
Abstract
 Add to MetaCart
(Show Context)
Discretization for naiveBayes learning: managing discretization bias and variance
Hierarchical Mixtures of Naive Bayesian Classifiers
"... Naive Bayesian classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this paper we study combining multiple naive Bayesian classifiers by using the hierarchical m ..."
Abstract
 Add to MetaCart
Naive Bayesian classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this paper we study combining multiple naive Bayesian classifiers by using the hierarchical mixtures of experts system. This system, which we call hierarchical mixtures of naive Bayesian classifiers, is compared to a simple naive Bayesian classifier and to using bagging and boosting for combining multiple classifiers. Results on 19 data sets from the UCI repository indicate that the hierarchical mixtures architecture in general outperforms the other methods.