scholarly journals Text Classification Based on Weighted Extreme Learning Machine

2019 ◽  
Vol 32 (1) ◽  
pp. 203 ◽  
Author(s):  
Hayder Mahmood Salman
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Text Classification ◽  
The Internet ◽  
Huge Amount ◽  
Text Documents ◽  
Feature Weights ◽  
Weighted Extreme Learning Machine ◽  
Proposed Model ◽  
Learning Machine

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

Incorporate Syntactic Information for Short Text Classification

2011 ◽  
Vol 268-270 ◽  
pp. 697-700
Author(s):  
Rui Xue Duan ◽  
Xiao Jie Wang ◽  
Wen Feng Li
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Environment ◽  
Text Classification ◽  
The Internet ◽  
Selection Methods ◽  
Text Documents ◽  
Short Text ◽  
Syntactic Information ◽  
Dependency Relations

As the volume of online short text documents grow tremendously on the Internet, it is much more urgent to solve the task of organizing the short texts well. However, the traditional feature selection methods cannot suitable for the short text. In this paper, we proposed a method to incorporate syntactic information for the short text. It emphasizes the feature which has more dependency relations with other words. The classifier SVM and machine learning environment Weka are involved in our experiments. The experiment results show that incorporate syntactic information in the short text, we can get more powerful features than traditional feature selection methods, such as DF, CHI. The precision of short text classification improved from 86.2% to 90.8%.

A Machine Learning-Based Lexicon Approach for Sentiment Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 8-22
Author(s):  
Tirath Prasad Sahu ◽  
Sarang Khandekar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Main Idea ◽  
Machine Learning Algorithms ◽  
Online Review ◽  
Product Reviews ◽  
Huge Amount ◽  
Text Documents ◽  
Proposed Model ◽  
The Web

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

An Optimization Strategy for Weighted Extreme Learning Machine based on PSO

2017 ◽  
Vol 31 (01) ◽  
pp. 1751001 ◽  
Author(s):  
Kai Hu ◽  
Zhaodi Zhou ◽  
Liguo Weng ◽  
Jia Liu ◽  
Lihua Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Nearest Neighbor ◽  
Training Data ◽  
Learning Approaches ◽  
Cognitive Computing ◽  
Weighted Extreme Learning Machine ◽  
Machine Systems ◽  
Learning Machine ◽  
Imbalance Learning

Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences. Among numerous machine learning algorithms, Weighted Extreme Learning Machine (WELM) is one of the famous cases recently. It not only has Extreme Learning Machine (ELM)’s extremely fast training speed and better generalization performance than traditional Neuron Network (NN), but also has the merit in handling imbalance data by assigning more weight to minority class and less weight to majority class. But it still has the limitation of its weight generated according to class distribution of training data, thereby, creating dependency on input data [R. Sharma and A. S. Bist, Genetic algorithm based weighted extreme learning machine for binary imbalance learning, 2015 Int. Conf. Cognitive Computing and Information Processing (CCIP) (IEEE, 2015), pp. 1–6; N. Koutsouleris, Classification/machine learning approaches, Annu. Rev. Clin. Psychol. 13(1) (2016); G. Dudek, Extreme learning machine for function approximation–interval problem of input weights and biases, 2015 IEEE 2nd Int. Conf. Cybernetics (CYBCONF) (IEEE, 2015), pp. 62–67; N. Zhang, Y. Qu and A. Deng, Evolutionary extreme learning machine based weighted nearest-neighbor equality classification, 2015 7th Int. Conf. Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2 (IEEE, 2015), pp. 274–279]. This leads to the lack of finding optimal weight at which good generalization performance could be achieved [R. Sharma and A. S. Bist, Genetic algorithm based weighted extreme learning machine for binary imbalance learning, 2015 Int. Conf. Cognitive Computing and Information Processing (CCIP) (IEEE, 2015), pp. 1–6; N. Koutsouleris, Classification/machine learning approaches, Annu. Rev. Clin. Psychol. 13(1) (2016); G. Dudek, Extreme learning machine for function approximation–interval problem of input weights and biases, 2015 IEEE 2nd Int. Conf. Cybernetics (CYBCONF) (IEEE, 2015), pp. 62–67; N. Zhang, Y. Qu and A. Deng, Evolutionary extreme learning machine based weighted nearest-neighbor equality classification, 2015 7th Int. Conf. Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2 (IEEE, 2015), pp. 274–279]. To solve it, a hybrid algorithm which composed by WELM algorithm and Particle Swarm Optimization (PSO) is proposed. Firstly, it distributes the weight according to the number of different samples, determines weighted method; Then, it combines the ELM model and the weighted method to establish WELM model; finally it utilizes PSO to optimize WELM’s three parameters (input weight, bias, the weight of imbalanced training data). Experiment data from both prediction and recognition show that it has better performance than classical WELM algorithms.

scholarly journals Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2615 ◽  
Author(s):  
Yang Du ◽  
Ke Yan ◽  
Zixiao Ren ◽  
Weidong Xiao
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Weather Conditions ◽  
Learning Technologies ◽  
Energy Harvest ◽  
Support Vector ◽  
Pv Systems ◽  
Weighted Extreme Learning Machine ◽  
Learning Machine ◽  
Power Point

A maximum power point tracker (MPPT) should be designed to deal with various weather conditions, which are different from region to region. Customization is an important step for achieving the highest solar energy harvest. The latest development of modern machine learning provides the possibility to classify the weather types automatically and, consequently, assist localized MPPT design. In this study, a localized MPPT algorithm is developed, which is supported by a supervised weather-type classification system. Two classical machine learning technologies are employed and compared, namely, the support vector machine (SVM) and extreme learning machine (ELM). The simulation results show the outperformance of the proposed method in comparison with the traditional MPPT design.

scholarly journals An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zi-Ji Yan
Keyword(s):  
Ion Channel ◽  
Extreme Learning Machine ◽  
Nuclear Receptor ◽  
Drug Target ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Weighted Extreme Learning Machine ◽  
Speed Up ◽  
Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

Sign in / Sign up

Export Citation Format

Share Document