FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors

Jiang, Jung-Yi; Tsai, Shian-Chi; Lee, Shie-Jue

doi:10.1016/j.eswa.2011.08.141

Cited by 71 publications

(34 citation statements)

References 21 publications

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“…Overall, the design of the system are made based on the research by Jiang et al [11] are shown in Figure 1 with dark-colored part is a contribution in this research. Based on Figure 1 above, this research method consists of four main phases, namely: automation of training data labeling, semantic relatedness measurement, Semantic-FSKNN training phase consisting of training pattern grouping and calculation of prior probability and likelihoods value, and the last is Semantic-FSKNN classification phase.…”

Section: Methodsmentioning

confidence: 99%

“…Semantic-FSKNN training phase consists of two phases: grouping patterns of training data and prior probability and likelihoods calculation [11].…”

Section: Semantic-fsknn Training Phasementioning

confidence: 99%

“…One research trying to perform multi-label classification of documents was done by Jung-Yi Jiang et al that proposed a method called Fuzzy Similarity based K-Nearest Neighbor (FSKNN) [11]. FSKNN method is proved to be better than other multi-label classification methods, but the FSKNN method has not taken into account the semantic factors in it.…”

Section: Introductionmentioning

confidence: 99%

“…The process of semantic relatedness measurement is done by using the method of HSO [9] [10] to get the semantic relationship between each class and each term that will be processed. While for the classification process, it uses FSKNN methods that have been introduced by Jiang et al [11] that there is a new variable addition in the training phase so that the value of semantic relatedness obtained in the previous process can be taken into consideration during the process of training. Classes is used for the process of classification is based on the international standard ISO/IEC9126.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

2015

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Section: Methodsmentioning

confidence: 99%

“…Semantic-FSKNN training phase consists of two phases: grouping patterns of training data and prior probability and likelihoods calculation [11].…”

Section: Semantic-fsknn Training Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

2015

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“…This is an important result because the proposed method was designed to optimize the above-mentioned quality indicator. Additionally, the introduced procedure can outperform the classification [29,30] and multimedia classification including classification of video objects [12], images [4,57] and music [43]. Another important field of application is bioinformatics where multi-label classification is a powerful tool for prediction of: gene functions [44], protein functions [55,56] or drug resistance [24], to name only a few.…”

Section: Introductionmentioning

confidence: 99%

An approximated decision-theoretic algorithm for minimization of the Tversky loss under the multi-label framework

Trajdos

Kurzyński

2017

Pattern Anal Applic

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Multi-label classification for colon cancer using histopathological images

Jiao

Wang

et al. 2013

Microsc. Res. Tech.

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Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.

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FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors

Cited by 71 publications

References 21 publications

Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

Classification of Non-Functional Requirements Using Semantic-FSKNN Based ISO/IEC 9126

An approximated decision-theoretic algorithm for minimization of the Tversky loss under the multi-label framework

Multi-label classification for colon cancer using histopathological images

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