Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method

Purpose The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic. Design/methodology/approach This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time. Findings Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method. Originality/value To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.

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Effective and efficient classification of gastrointestinal lesions: combining data preprocessing, feature weighting, and improved ant lion optimization

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02629-0 ◽

2020 ◽

Author(s):

Dalwinder Singh ◽

Birmohan Singh

Keyword(s):

Data Preprocessing ◽

Feature Weighting ◽

Ant Lion Optimization ◽

Gastrointestinal Lesions ◽

Combining Data ◽

Ant Lion

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Efficient Kernel Extreme Learning Machine and Neutrosophic C-means-based Attribute Weighting Method for Medical Data Classification

Journal of Circuits System and Computers ◽

10.1142/s0218126620502606 ◽

2020 ◽

Vol 29 (16) ◽

pp. 2050260 ◽

Cited By ~ 1

Author(s):

D. Shiny Irene ◽

T. Sethukarasi

Keyword(s):

Extreme Learning Machine ◽

Data Classification ◽

Medical Data ◽

Integrated System ◽

Weighting Method ◽

Attribute Weighting ◽

Kernel Extreme Learning Machine ◽

Medical Data Classification ◽

Learning Machine

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.

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