Evaluation of Multi-label Classifiers in Various Domains Using Decision Tree

Tidake, Vaishali S.; Sane, S. S.

doi:10.1007/978-981-10-7245-1_13

Cited by 2 publications

(15 citation statements)

References 9 publications

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“…In 2007, Grigorios T. and Ioannis K. [14] have categorized present MLC techniques into transformation and adaptation as shown in Table 2. The hierarchy is shown in Fig. 1 [14][15][16][17][18][19][20]. As the name indicates, transformation involves conversion of data from multiple labels to single label followed by single label classification (SLC).…”

Section: Taxonomy Of Multi-label Classificationmentioning

confidence: 99%

“…In 2009, Andre et al [16] categorized MLC methods based on dependency of algorithm. They formed two categories namely an algorithm independent method and an algorithm dependent method as shown in Fig. 2 [14][15][16][17][18][19][20]. The reported literature according to taxonomy shown in Fig.…”

Section: Taxonomy Of Multi-label Classificationmentioning

confidence: 99%

“…Finally for classification of new data, results of all the three classifiers for three labels are considered. The cons of the technique is that relation among different labels is simply ignored [18][19][20][21][22]. But it has many good features also.…”

Section: Binary Relevance (Br)mentioning

confidence: 99%

“…Multi-label Classification (MLC) is an act of allotting a set of predefined labels to an unseen entity by observing its characteristics. It's a supervised learning approach [20]. Classification is the most popular supervised data analysis approach and machine learning is widely used for it from many decades [36].…”

Section: Introductionmentioning

confidence: 99%

“…It has been used in various applications as listed in Table 1, like text categorization, image classification, graph classification, bioinformatics, functional genomics, emotion recognition, scene classification, semantic indexing of articles, mining social media, parallel tasks and multimedia annotation and many more [1][2][3][4][5][6][7][8][9][10][11][12]. Over the last two decades, lots of research papers, books and PhD theses have been published and various survey papers [14][15][16][17][18][19][20] are also available for the same. [11] Multimedia Annotation [13,15] The remaining contents are arranged in the following way.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multi-label Classification: a survey

Tidake¹,

Sane²

2018

IJET

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Wide use of internet generates huge data which needs proper organization leading to text categorization. Earlier it was found that a document describes one category. Soon it was realized that it can describe multiple categories simultaneously. This scenario reveals the use of multi-label classification, a supervised learning approach, which assigns a predefined set of labels to an object by looking at its characteristics. Earlier used in text categorization, but soon it became the choice of researchers for wide applications like marketing, multimedia annotation, bioinformatics. Two most common approaches for multi-label classification are transformation which takes the benefit of existing single label classifiers preceded by converting multi-label data to single label, or an adaptation which designs classifiers which handle multi-label data directly. Another popular approach is ensemble of multiple classifiers taking votes of all. Other approaches are also available namely algorithm independent and algorithm dependent approach. Based on results produced, suitable metric is used for example or label wise evaluation which depends on whether prediction is binary or ranking. Every approach offers benefits and issues like loss of label dependency in transformation, complexity in case of adaptation, improvement in results using ensemble which should be considered during design of underlying application.

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