Coupling Sentiment Dictionary and SVM Classification for Text Orientation Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 2444-2449
Author(s):  
Ming Ze Gao ◽  
Fang Fang Li ◽  
Zhe Yuan Ding ◽  
Wei Dong Xiao
Keyword(s):  
Classification Accuracy ◽  
Opinion Mining ◽  
Svm Classifier ◽  
Classification Methods ◽  
Initial Training ◽  
Training Set ◽  
Orientation Analysis ◽  
Second Stage ◽  
Sentiment Dictionary ◽  
Two Stages

Sentiment classification finds various applications in opinion mining, which can help users determine sentiment tendency of texts and information. In this paper, we consider the problem of text orientation analysis. In particular, we propose a two-stage approach by coupling sentiment dictionary and classification methods. In the first stage, we build sentiment dictionary and rules to obtain the texts whose emotional scores are ranked in the top 1/4 and the bottom 1/4. These texts are marked classified for supervising the second stage. In the second stage, we employ the SVM classifier to process the remaining texts. Finally, we combine the two stages to get the orientation analysis results for all the texts. Experimental results demonstrate that, in contrast to using sentiment dictionary and classification method separately, our proposed method achieves higher classification accuracy when an initial training set by manual tagging is unavailable.

EFFECTS OF SVM PARAMETER OPTIMIZATION BASED ON THE PARAMETER DESIGN OF TAGUCHI METHOD

2011 ◽  
Vol 20 (03) ◽  
pp. 563-575 ◽  
Author(s):  
MEI LING HUANG ◽  
YUNG HSIANG HUNG ◽  
EN JU LIN
Keyword(s):  
Taguchi Method ◽  
Parameter Design ◽  
Support Vector ◽  
Svm Classifier ◽  
Liver Disorders ◽  
Second Stage ◽  
Vector Machines ◽  
Class Labels ◽  
Two Stages ◽  
Decision Boundaries

Support Vector Machines (SVMs) are based on the concept of decision planes that define decision boundaries, and Least Squares Support Vector (LS-SVM) Machine is the reformulation of the principles of SVM. In this study a diagnosis on a BUPA liver disorders dataset, is conducted LS-SVM with the Taguchi method. The BUPA Liver Disorders dataset includes 345 samples with 6 features and 2 class labels. The system approach has two stages. In the first stage, in order to effectively determine the parameters of the kernel function, the Taguchi method is used to obtain better parameter settings. In the second stage, diagnosis of the BUPA liver disorders dataset is conducted using the LS-SVM classifier; the classification accuracy is 95.07%; the AROC is 99.12%. Compared with the results of related research, our proposed system is both effective and reliable.

A Novel Method Based on Ant Colony Optimization for Gene Selection

2013 ◽  
Vol 834-836 ◽  
pp. 1850-1853 ◽  
Author(s):  
Guang Di Cui ◽  
Gang Wang ◽  
Ying Li ◽  
Ji Zhang Fan
Keyword(s):  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Gene Selection ◽  
Information Gain ◽  
Ant Colony ◽  
Second Stage ◽  
Redundant Genes ◽  
Novel Method ◽  
Two Stages ◽  
Fuzzy Adaptive

In this paper, an ant colony optimization based method (AM) is proposed for gene selection. AM consists of two stages. In the first stage, some redundant genes are filtered by information gain (IG). In the second stage, a fuzzy adaptive ant colony optimization is applied to gene selection. We evaluate the performance of AM on five gene expression datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of AM with the results obtained from four existing well-known optimization algorithms. The comparison details show that AM could get better classification accuracy.

scholarly journals Study of the Effect of Breast Tissue Density on Detection of Masses in Mammograms

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
A. García-Manso ◽  
C. J. García-Orellana ◽  
H. M. González-Velasco ◽  
R. Gallardo-Caballero ◽  
M. Macías-Macías
Keyword(s):  
Breast Tissue ◽  
Screening Mammography ◽  
Svm Classifier ◽  
Tissue Composition ◽  
Cad System ◽  
Second Stage ◽  
Digital Database ◽  
Breast Tissue Density ◽  
Two Stages ◽  
Ongoing Project

One of the parameters that are usually stored for mammograms is the BI-RADS density, which gives an idea of the breast tissue composition. In this work, we study the effect of BI-RADS density in our ongoing project for developing an image-based CAD system to detect masses in mammograms. This system consists of two stages. First, a blind feature extraction is performed for regions of interest (ROIs), using Independent Component Analysis (ICA). Next, in the second stage, those features form the input vectors to a classifier, neural network, or SVM classifier. To train and test our system, the Digital Database for Screening Mammography (DDSM) was used. The results obtained show that the maximum variation in the performance of our system considering only prototypes obtained from mammograms with a concrete value of density (both for training and test) is about 7%, yielding the best values for density equal to 1, and the worst for density equal to 4, for both classifiers. Finally, with the overall results (i.e., using prototypes from mammograms with all the possible values of densities), we obtained a difference in performance that is only 2% lower than the maximum, also for both classifiers.

Altered Fine Structure of B Lymphocytes Correlated with Defective Terminal Differentiation

1973 ◽  
Vol 31 ◽  
pp. 386-387
Author(s):  
Dale E. Bockman ◽  
L. Y. Frank Wu ◽  
Alexander R. Lawton ◽  
Max D. Cooper
Keyword(s):  
Immune Deficiency ◽  
B Lymphocytes ◽  
Plasma Cells ◽  
Present Report ◽  
Specific Antigen ◽  
Terminal Differentiation ◽  
Second Stage ◽  
Two Stages ◽  
Deficiency Diseases ◽  
Cell Line Generation

B-lymphocytes normally synthesize small amounts of immunoglobulin, some of which is incorporated into the cell membrane where it serves as receptor of antigen. These cells, on contact with specific antigen, proliferate and differentiate to plasma cells which synthesize and secrete large quantities of immunoglobulin. The two stages of differentiation of this cell line (generation of B-lymphocytes and antigen-driven maturation to plasma cells) are clearly separable during ontogeny and in some immune deficiency diseases. The present report describes morphologic aberrations of B-lymphocytes in two diseases in which second stage differentiation is defective.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Analisis Kebutuhan Petunjuk Praktikum Berbasis Keterampilan Proses Sains untuk Mencapai Kemampuan Merancang Eksperimen

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Fitriah Khoirunnisa ◽  
Friska Septiani Silitonga ◽  
Veri Firmansyah
Keyword(s):  
Core Competence ◽  
Process Skills ◽  
Learning Materials ◽  
Science Process Skills ◽  
Second Stage ◽  
The Core ◽  
The Core Competence ◽  
Two Stages ◽  
Practical Instruction ◽  
Design Experiments

Penelitian ini bertujuan menganalisis kebutuhan petunjuk praktikum berbasis Keterampilan Proses Sains (KPS) untuk mencapai kemampuan merancang eksperimen pada materi kalor reaksi kalorimetri. Penelitian dilakukan terhadap peserta didik kelas XI SMA Negeri 2 Kota Tanjungpinang. Variabel penelitian mencakup analisis kebutuhan bahan ajar dan analisis kesesuaian Kompetensi Inti (KI) dan Kompetensi Dasar (KD). Jenis penelitian yang dilakukan adalah penelitian deskriptif kualitatif. Tahapan pertama dalam penelitian ini adalah menganalisis kebutuhan bahan ajar dengan cara membandingkan dua petunjuk praktikum yang selama ini telah digunakan di sekolah tersebut, ditinjau dari aspek struktur format penulisan, aspek kreativitas, dan aspek keterampilan proses sains yang terdapat dalam petunjuk praktikum. Sehingga didapatkan kesimpulan bahwa petunjuk praktikum yang selama ini digunakan tidak memberikan kesempatan kepada peserta didiknya untuk merancang eksperimen yang telah ditentukan. Tahapan kedua yaitu menganalisis kesesuaian kompetensi inti dan kompetensi dasar, yang bertujuan untuk menentukan indikator pencapaian kompetensi (IPK) yang akan menjadi acuan dalam mengembangkan petunjuk praktikum berbasis keterampilan proses sains. Dari kedua tahapan yang telah dilakukan maka dapat disimpulkan bahwa peserta didik memerlukan petunjuk praktikum yang mampu mengonstruksi pikiran dan mengaktifkan kinerja mereka, sehingga pendekatan Keterampilan Proses Sains menjadi pilihan dalam mengembangkan petunjuk praktikum yang sesuai dengan karakteristik kurikulum 2013.   This research aims to analyze the needs of Science Process Skills based Practical Instruction to achieve the ability to design experiments on the calor of reaction. This research was done to the students of class XI SMA Negeri 2 Tanjungpinang City. Research Variable includes the analysis of the needs of the learning materials and analysis of the suitability of the Core Competence (KI) and Basic Competence (KD). The type of research conducted is descriptive qualitative research. The first stages in this research is to analyze the needs of learning materials by comparing two practical instructions that had been implementing in the school, from the aspects of the structure of writing format, creativity, and science process skills embedded in practical instructions. The conclusion of this research that current practical instructions does not give an opportunity to the participants to design determined experiments. The second stage, namely analyzing the suitability of core competence and basic competence, which aims to determine the indicators of achievement of the competencies (GPA) which will be a reference in developing process skills-based teaching instructions science. Of the two stages that has been done then it can be concluded that learners need practical instructions to construct  thinking and and their performance, so the Science Process Skills approach is an option in developing practical instruction suitable for the characteristics of the curriculum of 2013.

ESTIMATING GAS AND ROCK OUTBURST RISK ON THE BASIS OF KNOWLEDGE AND EXPERIENCE – THE EXPERT SYSTEM BASED ON FUZZY LOGIC

2014 ◽  
Vol 59 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Norbert Skoczylas
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Mining Industry ◽  
Automatic Assessment ◽  
Science Field ◽  
Second Stage ◽  
Two Stages ◽  
Outburst Hazard

Abstract The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic.

Integrated assessment and mapping of the environmental situation in the Russian Federation regions

2017 ◽  
Vol 924 (6) ◽  
pp. 6-16
Author(s):  
V.S. Tikunov ◽  
O.Yu. Chereshnia
Keyword(s):  
Russian Federation ◽  
Pollution Index ◽  
Pollution Level ◽  
Anthropogenic Pressure ◽  
Environmental Capacity ◽  
Environmental Friendliness ◽  
Second Stage ◽  
Environmental Situation ◽  
The Russian Federation ◽  
Two Stages

The article presents a methodology for a comprehensive assessment of the environmental situation in Russian Federation regions based on the pollution index and the index of the ecological tension. The evaluation was carried out in two stages. At the first stage, the degree of pollution of the atmosphere, hydrosphere and lithosphere of the regions was estimated on the basis of the emission of pollutants into the atmosphere, departing from stationary sources, the formation of solid domestic wastes (SDW) and the discharge of contaminated wastewater. Based on these three indicators, a pollution index was constructed that estimates aggregate pollution level. In the second stage, the authors made the estimation of loads generated by atmospheric emissions, solid waste and waste water discharged into the territory of each region, per capita and in relation to the environmental capacity of the economy. This allows us to take into account the area of pollution, anthropogenic pressure and environmental responsibility of the population, as well as the environmental friendliness of production. On the basis of relative indicators, the index of ecological tension was created.

ER rule classifier with an optimization operator recommendation

2021 ◽  
pp. 1-13
Author(s):  
Xiaoyan Wang ◽  
Jianbin Sun ◽  
Qingsong Zhao ◽  
Yaqian You ◽  
Jiang Jiang
Keyword(s):  
Classification Accuracy ◽  
Comprehensive Analysis ◽  
Small Sample ◽  
Empirical Knowledge ◽  
Classification Methods ◽  
Turbofan Engine ◽  
Training Samples ◽  
Optimization Operator ◽  
Recommendation Strategy

It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

Sign in / Sign up

Export Citation Format

Share Document