A novel multi-label classification algorithm based on <i>K</i>-nearest neighbor and random walk

Wang, Zhenwu; Wang, Si-Kai; Wan, Benting; Song, William Wei

doi:10.1177/1550147720911892

Cited by 21 publications

(12 citation statements)

References 34 publications

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“…Multi-label classification focuses on how to model the oneto-many mapping between samples and labels, and how to make full use of the semantic relationship between labels to improve the classification accuracy [10], [11]. The existing methods can be roughly divided into two categories [12].…”

Section: B Multi-label Text Classificationmentioning

confidence: 99%

Generative Multi-Task Learning for Text Classification

et al. 2020

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Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. In this paper, a generative multi-task learning (MTL) approach for text classification and categorization is proposed, which is composed of a shared encoder, a multilabel classification decoder and a hierarchical categorization decoder. In the two decoders, a label-orderindependent multi-label classification loss function and a hierarchical structure mask matrix are introduced. Experiments conducted on the real-world public security dataset show that the proposed approach has obvious advantages over the baseline approaches and can enhance the semantic association between the results of the classification and categorization tasks. INDEX TERMS Generative model, hierarchical classification, multi-label classification, multi-task learning.

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Section: B Multi-label Text Classificationmentioning

confidence: 99%

Generative Multi-Task Learning for Text Classification

et al. 2020

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“…They proposed two techniques on the adaptation of local sets for multi-label data. One of them is aimed to clean the dataset and other is to reduce the dataset Random walk graph with KNN (MLRWKNN) [26] is yet another technique in this domain which works on the principal of graph theory by producing the set of vertices using a random walk for the nearest neighbor instances. It also generates the set of edges of label correlation.…”

Section: Ml-knnmentioning

confidence: 99%

Discriminative Adaptive Sets for Multi-Label Classification

2020

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Multi-label classification aims to associate multiple labels to a given data/object instance to better describe them. Multi-label data sets are common in a lot of emerging application areas like: Text/Multimedia classification, Bio-Informatics, Medical image annotations and Computer Vision to name a few. There is a growing interest in efficient and accurate multi-label classification. There are two major approaches to perform multi-label classification (i) problem transformation methods and (ii) algorithm adaptation methods. In algorithm adaptation, the traditional classification algorithms are modified to handle multi-label data sets. One classification algorithm which is often modified to do multi-label classification is k-nearest neighbor (kNN). k-nearest neighbor is popular due to its simplicity, easy to implement and seamlessly adaptability. Despite its merits it has several drawbacks like: sensitivity of noisy data, missing values and outliers; feature scaling and often becoming inaccurate for large overlapping solution space. In this paper, a modification to kNN method is suggested for multi-label classification with three improvement strategies (i) selection of local example w.r.t. unknown example-the motivation for this comes from the fact that local and relevant space is vital for the improvement in multi-label classification; (ii) Splitting the input space into multiple sub-spaces for optimal label estimation-the motivation is to estimate label accurately in the presence of noisy labels; And (iii) selection of labels using Mean Average Precision (MAP) estimates-here our motivation is to utilize the training data effectively to maximize the hidden distribution and optimal parameters for the method. The proposed method is implemented and compared with state-ofthe-art approaches based on kNN or similar approaches that effectively select and optimize relevant spaces for multi-label classification. Evaluation based on multiple metrics like Hamming loss, Precision/Recall and F-measure are used for evaluation. The suggested approach performed much better than the state-of-the-art on the datasets with strong label cardinalities.

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“…KNN linear classifier was used for supervised training using the collected datasets. There are few other recent solutions which make use of the pressure sensors and machine learning for identifying different postures [57][58][59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Smart Sleep Posture Recognition System

Tang

Kumar

Nadeem

et al. 2021

IoT

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Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%.

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A novel multi-label classification algorithm based on K-nearest neighbor and random walk

Cited by 21 publications

References 34 publications

Generative Multi-Task Learning for Text Classification

Generative Multi-Task Learning for Text Classification

Discriminative Adaptive Sets for Multi-Label Classification

CNN-Based Smart Sleep Posture Recognition System

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