scholarly journals A Swarm Intelligence Based Weighted Feature Extraction and Classification using SVM for Sentimental Exploration

2019 ◽  
Vol 8 (3) ◽  
pp. 883-890
Keyword(s):  
Swarm Intelligence ◽  
Fitness Function ◽  
Performance Measure ◽  
Support Vector ◽  
Relevant Feature ◽  
Data Set ◽  
Swarm Optimization ◽  
Recall Accuracy ◽  
Classification Technique ◽  
F Measure

The goal of Sentiment Exploration (SE) is used for mining the accurate sentiments which are very beneficial for businesses, governments, and individuals, the opinions, recommendations, ratings, and feedbacks are becoming an important aspect in present scenarios. The proposed methodology likewise attempts to introduce a swarm intelligence based sentimental supervised methodology. In order to obtain a relevant feature data set from a large number of data samples, this method used particle swarm optimization to attain the utmost optimum feature set. The evaluation of the optimum feature set is obtained by means of using Minimum Redundancy and Maximum Relevancy measure as the fitness function. The categorization of the extracted feature set is accomplished with the Support Vector Machine classification technique. The experimental outcome for the suggested method is evaluated using four performance measure like precision, recall, accuracy, and f-measure and showed that proposed swarm intelligent based classification method has better performance using IMDB, Movie Lens and Trip Advisor Data Samples.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Damage Identification for Simply-Supported Bridge Based on SVM Optimized by PSO (PSO-SVM)

2011 ◽  
Vol 80-81 ◽  
pp. 490-494 ◽  
Author(s):  
Han Bing Liu ◽  
Yu Bo Jiao ◽  
Ya Feng Gong ◽  
Hai Peng Bi ◽  
Yan Yi Sun
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Damage Identification ◽  
Fitness Function ◽  
Support Vector ◽  
Detection Accuracy ◽  
Damage Localization ◽  
Swarm Optimization ◽  
Simply Supported ◽  
Beam Bridge

A support vector machine (SVM) optimized by particle swarm optimization (PSO)-based damage identification method is proposed in this paper. The classification accuracy of the damage localization and the detection accuracy of severity are used as the fitness function, respectively. The best and can be obtained through velocity and position updating of PSO. A simply supported beam bridge with five girders is provided as numerical example, damage cases with single and multiple suspicious damage elements are established to verify the feasibility of the proposed method. Numerical results indicate that the SVM optimized by PSO method can effectively identify the damage locations and severity.

Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis

2012 ◽  
Vol 57 (5) ◽  
Author(s):  
Mohammad Reza Daliri
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Particle Swarm ◽  
Support Vector ◽  
Emission Computed Tomography ◽  
Binary Particle Swarm Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Vector Machines

AbstractIn this article, we propose a feature selection strategy using a binary particle swarm optimization algorithm for the diagnosis of different medical diseases. The support vector machines were used for the fitness function of the binary particle swarm optimization. We evaluated our proposed method on four databases from the machine learning repository, including the single proton emission computed tomography heart database, the Wisconsin breast cancer data set, the Pima Indians diabetes database, and the Dermatology data set. The results indicate that, with selected less number of features, we obtained a higher accuracy in diagnosing heart, cancer, diabetes, and erythematosquamous diseases. The results were compared with the traditional feature selection methods, namely, the F-score and the information gain, and a superior accuracy was obtained with our method. Compared to the genetic algorithm for feature selection, the results of the proposed method show a higher accuracy in all of the data, except in one. In addition, in comparison with other methods that used the same data, our approach has a higher performance using less number of features.

scholarly journals Implementasi Long Short-Term Memory untuk Identifikasi Berita Hoax Berbahasa Inggris pada Media Sosial

2020 ◽  
Vol 4 (2) ◽  
pp. 179-187
Author(s):  
Jasman Pardede ◽  
Raka Gemi Ibrahim
Keyword(s):  
Logistic Regression ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Recall Accuracy ◽  
Term Memory ◽  
Support Vector Classifier ◽  
Long Short Term Memory ◽  
F Measure

Hoax atau berita palsu menyebar sangat cepat di media sosial. Berita itu dapat memengaruhi pembaca dan menjadi racun pikiran. Masalah seperti ini harus diselesaikan secara strategis untuk mengidentifikasi berita yang dibaca yang disebarluaskan di media sosial. Beberapa metode yang diusulkan untuk memprediksi tipuan adalah menggunakan Support Vector Classifier, Logistic Regression, dan MultinomialNaiveBayes. Dalam studi ini, para peneliti menerapkan Long Short-Term Memory untuk mengidentifikasi hoax. Kinerja sistem diukur berdasarkan nilai precision, recall, accuracy, dan F-Measure. Berdasarkan hasil eksperimen yang dilakukan pada data tipuan diperoleh nilai rata-rata precision, recall, accuracy, dan F-Measure masing-masing 0,94, 0,96, 0,94, dan 0,95. Berdasarkan hasil eksperimen ditemukan bahwa Long Short-Term Memory yang diusulkan memiliki kinerja yang lebih baik dibandingkan dengan metode sebelumnya.

scholarly journals An Extended Hybrid Clustering Method Utilzing Svm as Cross Validator

2019 ◽  
Vol 9 (2) ◽  
pp. 721-726
Keyword(s):  
Hybrid Approach ◽  
Structured Data ◽  
Cosine Similarity ◽  
Unstructured Data ◽  
Support Vector ◽  
Massive Data ◽  
Recall Accuracy ◽  
Data Problem ◽  
Video And Audio ◽  
F Measure

The massive data accumulation from the internet creates attention for the researchers. The data collected in the form of structured and unstructured data. The structured data consists of messages, transactions, conversations, etc. while unstructured represents video and audio clips. This essentially manages the raw data problem in which unreferenced clustering is used. A hybrid approach is proposed using Cosine Similarity and soft cosine. A novel clustering technique is designed which is cross-validated using the Support Vector Machine (SVM). The validated approach is further verified by using K- means clustering. The clustering results have been further evaluated using parameters precision, recall, and F-measure. The evaluated results show the improvement in precision and recall accuracy due to hybridization of cosine similarity and soft cosine techniques

scholarly journals A Novel Multi-Parameter Tuned Optimizer for Information Retrieval based on Particle Swarm Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 1723-1731 ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Information Retrieval ◽  
Performance Metrics ◽  
Particle Swarm ◽  
Solution Space ◽  
Performance Measure ◽  
Data Set ◽  
Swarm Optimization ◽  
Multiple Input ◽  
Input Parameters

Tuning multi-parameter and parameter optimization in Information Retrieval has been a huge area of research and development, especially with BM25F scoring functions having a 2F+1 feature with F fields in the documents. The scoring and ranking function conventionally uses multiple input parameters, to augment the quality of results even at the value of huge calculation time. The searching and ranking documents in the medical literature encompass high recall rates, which are difficult to satisfy with multiple input parameters. The performance of the BM25F depends upon the choice of these F parameters. Particle Swarm Optimization (PSO) searches through the solution- space independently and discovers an optimal solution as opposed to improving and optimizing the gradient; henceforth it can straightforward optimize Mean Average Precision (MAP) a non-differentiable function. In this paper, the usage of PSO to tune multi-parameters is proposed to deal with the gaps in BM25Fscoring function. Also, the advantage of the proposed technique by directly optimizing the MAP has been discussed. Experimental results of quantitative performance metrics MAP and Mean Reciprocal Rank of the proposed PSO-optimized BM25F and most recent ranking algorithms have been compared. The performance measure results demonstrate that the proposed PSO-optimized BM25F performance measure outclasses the standard ranking methods for the OHSUMED data set

scholarly journals Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hsiou-Hsiang Liu ◽  
Lung-Cheng Chang ◽  
Chien-Wei Li ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Tourism Industry ◽  
Support Vector ◽  
Tourism Demand ◽  
Economic Sectors ◽  
Data Set ◽  
Swarm Optimization

The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS–PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS–PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS–PSOSVR is an effective method for forecasting tourism demand.

scholarly journals Alzheimer Disease Classification using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 6-9
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Developed Countries ◽  
Disease Classification ◽  
Support Vector ◽  
Data Set ◽  
Classification Technique ◽  
Routine Tasks ◽  
Memory Power ◽  
To Come

Alzheimer's disease is the most popular and persuading dementia that affects our memory power, reasoning and deportment. Symptoms rise up slowly and worsen with time, becoming an obstacle in doing our routine tasks. Alzheimer is not conventional wedge of aging. The substantial and known risk factor is up surging age. The prevalence of AD is depicted to be around 5% after an age of 65 years and took a leap of 30% for people of 85 years old in developed countries [1]. In this project we proposed a detection and classification technique using Random Forest(RF) and Support Vector Machine(SVM) algorithms on the oasis longitudinal data set and compare their respective accuracies to come to a conclusion that which algorithm best suits for this detection and classification. paper Setup must be in A4 size with Margin: Top 0.7”,

scholarly journals Research on PSO-ARMA-SVR Short-Term Electricity Consumption Forecast Based on the Particle Swarm Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenbo Zhu ◽  
Hao Ma ◽  
Gaoyan Cai ◽  
Jianwen Chen ◽  
Xiucai Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Arma Model ◽  
Small Sample ◽  
New Method ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Swarm Optimization

Aimed at the problem of order determination of short-term power consumption in a time series model, a new method was proposed to determine the order p and the moving average q of the ARMA model by particle swarm optimization (PSO).According to the difference between the predicted value and the real value of the ARMA model, the fitness function of the particle swarm optimization algorithm is constructed, while the optimal solution which satisfies the ARMA model is confirmed by adjusting the inertia weight, population size, particle velocity, and iteration number. Finally, SVR regression is performed by using a support vector machine to correct the residual sequence obtained after the prediction of ARMA. The final prediction result is obtained by adding the predicted values and corrected residual. Based on the data of historical electricity load of a residential district in 2016~2017, the proposed method is compared with the traditional models. The result of the use of MATLAB simulation shows that the method is simple and feasible, greatly improves the model prediction accuracy, and implements the new method for short-term load forecasting of a small sample.

PSO-SVM-based gait phase classification during human walking on unstructured terrains: Application in lower-limb exoskeleton

2019 ◽  
Vol 233 (19-20) ◽  
pp. 7144-7154
Author(s):  
Jiajia Zheng ◽  
Jianhua Chen ◽  
Mingxing Yang ◽  
Song Chen
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Particle Swarm ◽  
Support Vector ◽  
Data Set ◽  
Swarm Optimization ◽  
Phase Classification ◽  
Gait Phase

Gait analysis is of great importance to ensure that gait phases induced by robotic exoskeleton are tailored to each individual and external complex environments. The objective of this work is to develop a pressure insole system with redundant functionality for gait phase classification based on the analysis of ground reaction force on unstructured terrains. A support vector machine optimized by particle swarm optimization was proposed for classifying four gait phases including initial contact, mid stance, terminal stance and swing phase. Seven pressure sensors are employed according to the plantar distribution contour of ground reaction force and walking data acquisition is conducted on treadmill, concrete pavement and wild grassland, respectively. Two classifiers, support vector machine-based classifier I and PSO-SVM-based classifier II are constructed on the basis of gait data set obtained on treadmill. Experimental results showed that the proposed PSO-SVM algorithm exhibits distinctive advantages on gait phase classification and improves the classification accuracy up to 32.9%–42.8%, compared with that of classifier based solely on support vector machine. In addition, some unwanted errors, intentional attacks or failures can be successfully solved with fast convergence rate and good robustness by using particle swarm optimization.

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