Knowledge Discovery in Multi-label Phenotype Data

By Amanda Clare, Ross D. King. European Conference on Principles of Data Mining and Knowledge Discovery, 2001

792 citations recorded by the source corpus.

Subfields: Information Systems, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e64ef24d0f6a9cefd7bf7a6b1d5f34f90ec37939

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In Molecular Biology

Citations in the corpus

Ieee Transactions on Knowledge and Data Engineering 1 Multi-label Neural Networks with Applications to Functional Genomics and Text Categorization (positive): “As reviewed above, most of the existing multi-label learning algorithms are derived from traditional learning techniques such as probabilistic generative models [18], [30], boosting methods [28], decision trees [4], [5], and maximal margin methods [2], [8], [16].”
Learning multi-label scene classification (2004, methodology): “In bioinformatics , Clare and King [4] extended the deÿnition of entropy to include multi-label data (gene expression in their case), but they used a decision tree as their baseline algorithm algorithm .”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Multi-label decision trees and random forests have also been well studied [6,15,25,26, 34,42,45].”
Combining instance-based learning and logistic regression for multilabel classification (2009, background): “…by speciﬁc types of applications such as text categorization [ Schapire and Singer, 2000; Ueda and Saito, 2003; Kazawa et al. , 2005; Zhang and Zhou, 2006], computer vision [Boutell et al. , 2004], and bioinformatics [Clare and King, 2001; Elis-seeff and Weston, 2002; Zhang and Zhou, 2006].”
Lift: Multi-Label Learning with Label-Specific Features (2011, methodology): “Based on those training examples, f k can be induced with popular learning techniques such as AdaBoost [40], k-nearest neighbor [58], kernel methods [3], decision trees [8], [9], etc.”
Multilabel Classification with Principal Label Space Transformation (2012, neutral): “MH (Schapire & Singer, 2000), Rank-SVM (Elisseeﬀ & Weston, 2001), Multi-label C4.5 (Clare & King, 2001) and ML-KNN (Zhang & Zhou, 2007).”
Feature selection for multi-label naive Bayes classification (2009, background): “Although the generality of multi-label problems inevitably makes it more difficult to learn, researchers have proposed a number of algorithms to learn from multi-label instances, such as multi-label decision trees [8,9], multi-label neural networks [10,45], and multi-label kernel methods [2,12,17,23”
Multi-Label Classification: An Overview (2007, background): “Each of these classes is then subdivided into more specific classes, and these are in turn subdivided, and then again subdivided, so the hierarchy is up to 4 levels deep (Clare & King, 2001).”
A k-nearest neighbor based algorithm for multi-label classification (2005, background): “Fortunately, several approaches specially designed for multi-label classification have been proposed, such as multi-label text categorization algorithms [1], [2], [3], multi-label decision trees [4], [5] and multi-label kernel method [6], etc.”
SGM: Sequence Generation Model for Multi-label Classification (2018, methodology): “Other methods such as ML-DT (Clare and King, 2001), Rank-SVM (Elisseeff and Weston, 2002), and ML-KNN (Zhang and Zhou, 2007) can only be used to capture the first or second order label correlations or are computationally intractable when high-order label correlations are considered.”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “In the algorithm adaptation approach there are proposals of multilabel C4.5 trees [14], algorithms based on nearest neighbors such as ML-kNN [15], multilabel neural networks [2] [16], and multilabel SVMs [17].”
Label Distribution Learning (2013, background): “For exampl e, it can be extended fromk-NN [37], [5], decision tree [6], [24], or neural networks [36], [27].”
Multi-Label Prediction via Compressed Sensing (2009, background): “class hierarchy), efficient learning and prediction methods are often possible [5, 6, 7, 8, 9].”
Multilabel relationship learning (2013, background): “…[Schapire and Singer 2000]; Clare and King extended decision trees for multilabel learning by generalizing the de.nition of entropy [Clare and King 2001]; Zhang and Zhou proposed a multilabel neural network by assuming that different labels share the same hidden representation…”
Mining Multi-label Data (2010, methodology): “Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization i”
Multi-instance multi-label learning (2008, methodology): “In addition to text categorization, multi-label learning has also been found useful in many other tasks such as scene classification [11], image and video annotation [38,48], bioinformatics [7,12,13,21, 27], and even association rule mining [50, 63].”
ML-KNN: A lazy learning approach to multi-label learning (2007, methodology): “Clare and King [5] adapted C4.5 decision tree [18] to handle multi-label data (gene expression in their case) through modifying the definition of entropy.”
Ensemble application of convolutional and recurrent neural networks for multi-label text categorization (2017, neutral): “For algorithm adaptation, ML-KNN [19], ML-DT [20], and Rank-SVM [21] adapt popular classification algorithms, e.g., k-nearest-neighbors, decision trees, support vector machines, to solve multi-label classification problems.”
Multi-class pattern classification using neural networks (2007, background): “The OAO system's performance even surpassed the published prediction accuracy range of 60–75% [6] [7].”