Multi-task Fuzzy Clustering–Based Multi-task TSK Fuzzy System for Text Sentiment Classification

2022 ◽  
Vol 21 (2) ◽  
pp. 1-24
Author(s):  
Xiaoqing Gu ◽  
Kaijian Xia ◽  
Yizhang Jiang ◽  
Alireza Jolfaei
Keyword(s):  
Language Processing ◽  
Fuzzy Clustering ◽  
Private Information ◽  
Fuzzy System ◽  
Clustering Algorithm ◽  
Public Information ◽  
Sentiment Classification ◽  
Fuzzy Rules ◽  
Proposed Model ◽  
The Common

Text sentiment classification is an important technology for natural language processing. A fuzzy system is a strong tool for processing imprecise or ambiguous data, and it can be used for text sentiment analysis. This article proposes a new formulation of a multi-task Takagi-Sugeno-Kang fuzzy system (TSK FS) modeling, which can be used for text sentiment image classification. Using a novel multi-task fuzzy c-means clustering algorithm, the common (public) information among all tasks and the individual (private) information for each task are extracted. The information about clustering, for example, cluster centers, can be used to learn the antecedent parameters of multi-task TSK fuzzy systems. With the common and individual antecedent parameters obtained, a corresponding multi-task learning mechanism for learning consequent parameters is devised. Accordingly, a multi-task fuzzy clustering–based multi-task TSK fuzzy system (MTFCM-MT-TSK-FS) is proposed. When the proposed model is built, the information conveyed by the fuzzy rules formed is two-fold, including (1) common fuzzy rules representing the inter-task correlation information and (2) individual fuzzy rules depicting the independent information of each task. The experimental results on several text sentiment datasets demonstrate the validity of the proposed model.

Study of Combined Fuzzy Clustering Algorithm Based on F-Statistics Hierarchy Clustering

2010 ◽  
Vol 29-32 ◽  
pp. 802-808
Author(s):  
Min Min
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cluster Center ◽  
Evaluation Data ◽  
Fuzzy Clustering Algorithm ◽  
Initial Cluster ◽  
The Common ◽  
Common Problems ◽  
F Statistics

On analyzing the common problems in fuzzy clustering algorithms, we put forward the combined fuzzy clustering one, which will automatically generate a reasonable clustering numbers and initial cluster center. This clustering algorithm has been tested by real evaluation data of teaching designs. The result proves that the combined fuzzy clustering based on F-statistic is more effective.

scholarly journals Enhanced Weight Based Convolutional Neural Network (EWCNN) and Fuzzy Clustering For Semantically Rich Multi-Label Social Emotion Classification

2019 ◽  
Vol 8 (12) ◽  
pp. 4112-4121
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Fuzzy Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Novel Mutation ◽  
Experimental Result ◽  
Emotion Classification ◽  
Social Emotion

In Recent Years, Social Emotion In Recent Years Acquires Natural Language Processing Researchers’ Attention, Because Of Analyzing User-Generated Emotional Documents On The Web. But, These Emotions Has Noisy Instance Mixed And It Is Great Dispute To Acquire The Textual Meaning Of Short Messages. Definition: In General, Large-Scale Datasets Will Have Many Noisy Data, Which Can’t Be Used Readily And Also It Is Costly, Because Of Ambiguity Of Various Informal Expressions In User-Generated Comments. It Is Very Tedious One To Recognize The Similar User Documents From The Entire Social Media Text Message. Furthermore, Online Comments Are Characteristically Categorized By A Sparse Feature Space, Which Makes The Respective Emotion Classification Task A Complex One. Methodology: Three Major Contributions Were Done In This Work In Order To Rectify These Problems, They Are: Development Of A Novel Mutation Bat Optimization Based Sparse Encoding (MBO-SC) Which Transforming The Sparse Low-Level Features Into Dense HighLevel Features, Was The 1st Contribution, Next Is, An Enhanced Weight Based Convolutional Neural Network (EWCNN) To Target-Specific Layer. It Influences The Semantically EWCNN Classifier To Include Semantic Domain Knowledge Into The Neural Network To Bootstrap Its Inference Power And Interpretability. Fuzzy Clustering Algorithm Is Proposed To Minimize The Similarity Among Two Documents. Uses: It Is Quite Constructive In Recommending Products, Collecting Public Opinions, And Predicting Election Results. Proposed Work Is Distinguished With The Existing Methods, With The Metrics Such As: Precision, Recall, Sensitivity, Specificity, FMeasure And Accuracy. From The Experimental Result It Is Confirmed That The Quality Of Learned Semantic Vectors And The Performance Of Social Emotion Classification Can Be Enhanced By Proposed Models.

scholarly journals Legislative coalitions with incomplete information

2017 ◽  
Vol 114 (11) ◽  
pp. 2876-2880 ◽  
Author(s):  
Tiberiu Dragu ◽  
Michael Laver
Keyword(s):  
Incomplete Information ◽  
Private Information ◽  
Common Knowledge ◽  
Public Information ◽  
Theoretical Framework ◽  
The Other ◽  
Policy Preferences ◽  
Electoral Rules ◽  
Parliamentary Democracies ◽  
The Common

In most parliamentary democracies, proportional representation electoral rules mean that no single party controls a majority of seats in the legislature. This in turn means that the formation of majority legislative coalitions in such settings is of critical political importance. Conventional approaches to modeling the formation of such legislative coalitions typically make the “common knowledge” assumption that the preferences of all politicians are public information. In this paper, we develop a theoretical framework to investigate which legislative coalitions form when politicians’ policy preferences are private information, not known with certainty by the other politicians with whom they are negotiating over what policies to implement. The model we develop has distinctive implications. It suggests that legislative coalitions should typically be either of the center left or the center right. In other words our model, distinctively, predicts only center-left or center-right policy coalitions, not coalitions comprising the median party plus parties both to its left and to its right.

Wind Speed Prediction Model Based on Radial Basis Functional Neural Network

2011 ◽  
Vol 383-390 ◽  
pp. 5656-5662
Author(s):  
Tao Ding ◽  
Hong Fei Xiao
Keyword(s):  
Wind Speed ◽  
Fuzzy Clustering ◽  
Power Plants ◽  
Clustering Algorithm ◽  
Wind Speed Prediction ◽  
Wind Power Plants ◽  
Proposed Model ◽  
Radial Basis ◽  
Functional Neural Network ◽  
Speed Prediction

Wind speed forecasting is important to operation of wind power plants and power systems. To solve short term wind speed prediction problem, a radial basis functional neural network prediction model for wind speed time series based on cross iterative fuzzy clustering algorithm and regularized orthogonal least squares algorithm is proposed. First, the optimal fuzzy clustering centers of samples are computed by cross iterative fuzzy clustering algorithm. Then radial basis functional centers are optimized by regularized orthogonal least squares algorithm, and the generalized cross-validation is regarded as criteria to halt center selection. The proposed model centralizes advantages of both algorithms, and it can decrease network scale, improve generalization performance, accelerate network training speed and avoid ill-conditioning of learning problems. A case of practical wind speed time series from wind power plants verifies validity of the proposed model.

Sign in / Sign up

Export Citation Format

Share Document