Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Liwen, Peng; Liu, Yongguo

doi:10.1155/2018/2814897

Cited by 8 publications

(20 citation statements)

References 30 publications

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“…Peng and Liu 3 investigated the accuracy in terms of hamming loss and performance in terms of F1 score of multilabel feature selection in multilabel datasets. This is done using multilabel feature selection method called mutual information (MI) technique that led to hamming loss value (less is better) of 0.1986 and F1 score value (more is better) of 0.6650 in emotion dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Peng and Liu 3 investigated the accuracy in terms of hamming loss and performance in terms of F1 score of multilabel classification in multilabel datasets. This is done using Overlapping Clustering Based Multilabel Classification (OCBMLC) which combines FCM clustering and Multilabel Classification based K-Nearest Neighbor (ML-KNN) classifier that lead to hamming loss value (less is better) of 0.1983 and F1 score value (more is better) of 0.6745 in emotion dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

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RETRACTED: A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification

Subedi¹,

Alsadoon²,

Prasad

et al. 2019

Health Informatics J

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Medical diagnosis through classification is often critical as the medical datasets are multilabel in nature, that is, a patient may have more than one health condition: high blood pressure, obesity, and diabetes. The aim of this article is to improve the accuracy and performance of multilabel classification using multilabel feature selection and improved overlapping clustering method. The proposed system consists of Optimized Initial Cluster Centers and Enhanced Objective Function technique to reduce the number of iterations in the clustering process thereby improving the clustering performance and to improve the clustering accuracy which will result in improving the accuracy and performance of multilabel classification. Ratios of clustering distance to class distance and execution time are used as the evaluation metric for accuracy and total execution time is used as the evaluation metric for performance. Based on the different combination with the number of labels, attributes, instances, and number of clusters, different values of accuracy and performance are obtained. The results on all 10 datasets show that the proposed technique is superior to the current technique. Furthermore, on average, the proposed technique has improved the classification accuracy by 5%–7%. Furthermore, the performance of new technique is improved by decreasing the processing time by 0.5–1 s on average. The proposed system targets on improving the accuracy and performance of the multilabel classification for medical diagnosis, which consists of multilabel feature selection and enhanced overlapping clustering technique. This study provides an acceptable range of accuracy with improved processing time, which assists the doctors in medical diagnosis (high blood pressure, obesity, and diabetes) of patients.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification

Subedi¹,

Alsadoon²,

Prasad

et al. 2019

Health Informatics J

View full text Add to dashboard Cite

show abstract

“…A process of measuring the quality of clustering results is known as cluster validation, and it plays an important role in determining the performance of a clustering algorithm (Peng & Liu, ). Cluster validation may be of two types, which are external clustering validation and internal clustering validation.…”

Section: Proposed Workmentioning

confidence: 99%

A clustering‐based feature selection framework for handwritten Indic script classification

et al. 2019

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In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well.

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“…Liwen Peng (2018). It propose a multilabel feature selection algorithm as a preprocessing stage before Multilabel Classification (MLC).…”

Section: Literature Reviewmentioning

confidence: 99%

Enhance Clustering Approach using PSO-A* for E-Commerce

Pandey¹,

Soni²

2019

IJCA

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In the current trends most of the people uses online searching and purchasing the items. So web data size is drastically increases and after some time not possible to put all data in memory. So there is a need to manage data access speed and also increase the speed of searching. In the existing system most of clustering algorithm uses only basic criteria for machine learning and data mining algorithms. Initially it is not able to select the good center point so it will do to increase the number of iteration. Second problem is to access of full database and third problem is needed to increase searching speed. The proposed approach uses PSO-A* algorithm for clustering and searching that is used for select the good center point initial for clustering. After that only clustered data is access not full database that make partial database access to manage memory management and speed. Finally third approach is uses for increase searching speed. Now the proposed approach is to perform well and improve the ecommerce website performance.

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Feature Selection and Overlapping Clustering-Based Multilabel Classification Model

Cited by 8 publications

References 30 publications

RETRACTED: A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification

RETRACTED: A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification

A clustering‐based feature selection framework for handwritten Indic script classification

Enhance Clustering Approach using PSO-A* for E-Commerce

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