Multi-Label Classification

Tsoumakas, Grigorios; Katakis, Ioannis

doi:10.4018/978-1-59904-951-9.ch006

Cited by 96 publications

(116 citation statements)

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“…Because many proteins are multi-functional, classifiers should be able to assign a sequence to multiple, not mutually exclusive, classes (the multi label classification problem), or more generally, to a subset of nodes in a directed-acyclic graph, e.g., the GO hierarchy, (the structured label classification problem). Fortunately, a number of research groups have developed machine learning algorithms for multi-label and structured label classification and demonstrated their application in large-scale protein function classification [30-33]. We can draw on recent advances in machine learning methods for hierarchical multi-label classification of large sequence datasets to adapt our method to work in such a setting.…”

Section: Discussionmentioning

confidence: 99%

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

2007

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BackgroundIncorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.ResultsIn a set of 211 previously annotated mouse protein kinases, we found that 201 of the GO annotations returned by AmiGO appear to be inconsistent with the UniProt functions assigned to their human counterparts. In contrast, 97% of the predicted annotations generated using a machine learning approach were consistent with the UniProt annotations of the human counterparts, as well as with available annotations for these mouse protein kinases in the Mouse Kinome database.ConclusionWe conjecture that most of our predicted annotations are, therefore, correct and suggest that the machine learning approach developed here could be routinely used to detect potential errors in GO annotations generated by high-throughput gene annotation projects.Editors Note : Authors from the original publication (Okazaki et al.: Nature 2002, 420:563–73) have provided their response to Andorf et al, directly following the correspondence.

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Section: Discussionmentioning

confidence: 99%

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

2007

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“…We can describe the implementation of coding systems for text as multiple-label classification problems where multiple codes are attached to each document [28]. Machine learning approaches for automatic multiple-label document classification have been successfully used in various domains [29]–[31], [35], [36], including medical applications for disease diagnosis and medical error detection [37]–[39].…”

Section: Introductionmentioning

confidence: 99%

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models

Gaut

Steyvers

Imel

et al. 2017

IEEE J. Biomed. Health Inform.

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Psychotherapy represents a broad class of medical interventions received by millions of patients each year. Unlike most medical treatments, its primary mechanisms are linguistic; i.e., the treatment relies directly on a conversation between a patient and provider. However, the evaluation of patient-provider conversation suffers from critical shortcomings, including intensive labor requirements, coder error, non-standardized coding systems, and inability to scale up to larger data sets. To overcome these shortcomings, psychotherapy analysis needs a reliable and scalable method for summarizing the content of treatment encounters. We used a publicly-available psychotherapy corpus from Alexander Street press comprising a large collection of transcripts of patient-provider conversations to compare coding performance for two machine learning methods. We used the Labeled Latent Dirichlet Allocation (L-LDA) model to learn associations between text and codes, to predict codes in psychotherapy sessions, and to localize specific passages of within-session text representative of a session code. We compared the L-LDA model to a baseline lasso regression model using predictive accuracy and model generalizability (measured by calculating the area under the curve (AUC) from the receiver operating characteristic (ROC) curve). The L-LDA model outperforms the lasso logistic regression model at predicting session-level codes with average AUC scores of .79, and .70, respectively. For fine-grained level coding, L-LDA and logistic regression are able to identify specific talk-turns representative of symptom codes. However, model performance for talk-turn identification is not yet as reliable as human coders. We conclude that the L-LDA model has the potential to be an objective, scaleable method for accurate automated coding of psychotherapy sessions that performs better than comparable discriminative methods at session-level coding and can also predict fine-grained codes.

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“…The 4 metrics results of BR-KNN, BR-C4.5, BR-NB, BR-SMO on scene, genbase, and yeast data are extractedfrom [13], others are ran by using Weka [6] and Mulan [11] implementations, and we also implemented BR-RDT and LP-RDT algorithms based on these frameworks. Because RAKEL algorithm can not accomplish the calculation in 8 hours on genbase, yeast and enron, no results were provided here.…”

Section: Results On Small Datasetsmentioning

confidence: 99%

“…Tsoumakas and Katakis [9] summarized them into two categories: problem transformation and algorithm adaptation.…”

Section: Related Workmentioning

confidence: 99%

“…We select five small datasets (genbase, yeast, scene, emotions, and enron) and one large dataset (delicious) from Mulan site [11], and make sure they cover different domains and carry different statistic characteristics. Among them, genbase and yeast are biological datasets with regard to protein function classification and gene function classification respectively.…”

Section: Datasetsmentioning

confidence: 99%

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Multi-label Classification without the Multi-label Cost

Zhang¹,

Quan²,

Zhao³

et al. 2010

Proceedings of the 2010 SIAM International Conference on Data Mining

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Multi-label classification, or the same example can belong to more than one class label, happens in many applications. To name a few, image and video annotation, functional genomics, social network annotation and text categorization are some typical applications. Existing methods have limited performance in both efficiency and accuracy. In this paper, we propose an extension over decision tree ensembles that can handle both challenges. We formally analyze the learning risk of Random Decision Tree (RDT) and derive that the upper bound of risk is stable and lower bound decreases as the number of trees increases. Importantly, we demonstrate that the training complexity is independent from the number of class labels, a significant overhead for many state-of-the-art multi-label methods. This is particularly important for problems with large number of multi-class labels. Based on these characteristics, we adopt and improve RDT for multi-label classification. Experiment results have demonstrated that the computation time of the proposed approaches is 1-3 orders of magnitude less than other methods when handling datasets with large number of instances and labels, as well as improvement up to more than 10% in accuracy as compared to a number of state-of-the-art methods in some datasets for multi-label learning. Considering efficiency and effectiveness together, Multi-label RDT is the top rank algorithm in this domain. Even compared with the HOMER algorithm proposed to solve the problem of large number of labels, Multi-label RDT runs 2-3 orders of magnitude faster in training process and achieves some improvement on accuracy. Software and datasets are available from the authors.

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Multi-Label Classification

Cited by 96 publications

References 0 publications

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

Content Coding of Psychotherapy Transcripts Using Labeled Topic Models

Multi-label Classification without the Multi-label Cost

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