Results 1 - 10 of 55

Document-base Extraction for Single-label Text Classification

by Yanbo J. Wang, Robert S, Frans Coenen, Paul Leng
"... Abstract. Many text mining applications, especially when investigating Text Classification (TC), require experiments to be performed using common textcollections, such that results can be compared with alternative approaches. With regard to single-label TC, most text-collections (textual data-source ..."
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Abstract. Many text mining applications, especially when investigating Text Classification (TC), require experiments to be performed using common textcollections, such that results can be compared with alternative approaches. With regard to single-label TC, most text-collections (textual data

Normalization of microarray data: single-labeled and dual-labeled arrays

by Jin Hwan Do - Molecules and Cells , 2006
"... DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray ex-periments. Normalization is a critical step for obtaining data that are reliable an ..."
Abstract - Cited by 16 (0 self) - Add to MetaCart
, the knowledge of underlying assumption and principle of normaliza-tion would be helpful for the correct analysis of mi-croarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing

Mining multi-label data

by Grigorios Tsoumakas, Ioannis Katakis, Ioannis Vlahavas - In Data Mining and Knowledge Discovery Handbook , 2010
"... A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such d ..."
Abstract - Cited by 92 (9 self) - Add to MetaCart
A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L

A Novel Single-Label Supervised Text Classification Approach based on Mining Association Rules

by Yanbo Wang, Frans Coenen, Paul Leng
"... In this paper, we introduce a novel single-label supervised text classification approach based on mining association rules, called Apriori-TFP-TC. We follow the common framework of text mining in general, separating text classification into two stages, (1) text preprocessing and (2) the utilization ..."
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In this paper, we introduce a novel single-label supervised text classification approach based on mining association rules, called Apriori-TFP-TC. We follow the common framework of text mining in general, separating text classification into two stages, (1) text preprocessing and (2) the utilization

Decision trees for hierarchical multi-label classification

by Celine Vens, Jan Struyf, Er Schietgat, Hendrik Blockeel - Machine Learning , 2008
"... Abstract. Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an em ..."
Abstract - Cited by 59 (2 self) - Add to MetaCart
as an empirical study of their use in functional genomics. We compare learn-ing a single HMC tree (which makes predictions for all classes together) to two approaches that learn a set of regular classification trees (one for each class). The first approach defines an independent single-label clas-sification task

On the Stratication of Multi-Label Data

by Konstantinos Sechidis, Grigorios Tsoumakas, Ioannis Vlahavas
"... Abstract. Stratied sampling is a sampling method that takes into account the existence of disjoint groups within a population and pro-duces samples where the proportion of these groups is maintained. In single-label classication tasks, groups are dierentiated based on the value of the target variabl ..."
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Abstract. Stratied sampling is a sampling method that takes into account the existence of disjoint groups within a population and pro-duces samples where the proportion of these groups is maintained. In single-label classication tasks, groups are dierentiated based on the value of the target

LEARNING FROM MULTI-LABEL DATA

by Grigorios Tsoumakas, Min-ling Zhang , 2009
"... This volume contains research papers accepted for presentation at the 1st International Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled, Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009. MLD’09 is devoted to multi-label learning, which is an emerging ..."
Abstract - Cited by 11 (2 self) - Add to MetaCart
This volume contains research papers accepted for presentation at the 1st International Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled, Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009. MLD’09 is devoted to multi-label learning, which is an emerging

R: Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 2005; 6: 27

by Kevin Dobbin, Richard Simon
"... Determining sample sizes for microarray experiments is important but the complexity of these experiments, and the large amounts of data they produce, can make the sample size issue seem daunting, and tempt researchers to use rules of thumb in place of formal calculations based on the goals of the ex ..."
Abstract - Cited by 48 (9 self) - Add to MetaCart
Determining sample sizes for microarray experiments is important but the complexity of these experiments, and the large amounts of data they produce, can make the sample size issue seem daunting, and tempt researchers to use rules of thumb in place of formal calculations based on the goals

Multi-label learning by exploiting label dependency

by Min-ling Zhang, Kun Zhang - In KDD , 2010
"... In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (expo-nential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefor ..."
Abstract - Cited by 56 (2 self) - Add to MetaCart
as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient proce-dure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a

Mining Multi-label Data Streams Using Ensemble-based Active Learning

by Peng Wang, Peng Zhang, Li Guo
"... Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused on mining from single-label data streams. Mining from multi-label data strea ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Data stream classification has drawn increasing attention from the data mining community in recent years, where a large number of stream classification models were proposed. However, most existing models were merely focused on mining from single-label data streams. Mining from multi-label data
Results 1 - 10 of 55