Data Mining and Soft Computing using Support Vector Machine: A Survey
نویسندگان
چکیده
In this paper, the basic concepts and survey of the available literature on Support Vector Machines (SVM) in data mining and soft computing research area is provided. While at the time of survey several new methods were found related to SVM like as Support Vector Representation and Discrimination Machine (SVRDM), Recursive SVM (RSVM), On-line Independent SVM (OISVM), Pruning SVM, Fast Nearest Neighbor Condensation classifier (FCNN-SVM), Improved SV Clustering (iSVC), Cost-sensitive SVM (2v-SVM), 2C-SVM, Profile SVM (PSVM), Twin SVM (TWSVM), Twin Bounded SVM (TBSVM), Parametric-margin n-SVM (par-n-SVM), Twin Parametric-Margin SVM (TPMSVM), Structural Twin SVM (S-TWSVM), Hierarchical Linear SVM (H-LSVM), Bio-SVM, FuzzySVM-CIL, Kernel Fuzzy C-Means clustering-based Fuzzy SVM (KFCM-FSVM), Multi-Class Instance Selection (MCIS). After studied these methods a comparative and analytical survey upon those methods are presented here. Also a large future scope is available on several techniques and they are discussed in this paper.
منابع مشابه
Data Mining, Soft Computing, Machine Learning and Bio-Inspired Computing for Heart Disease Classification / Prediction– A Review
Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patient’s life. In this survey research article we aim to portray various data mining algorithms, soft computing techniques, machine learning algorithms and bio-inspired algorithms for predicting / classifying heart disease. Several ...
متن کاملHigh performance of the support vector machine in classifying hyperspectral data using a limited dataset
To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size ...
متن کاملBubble Pressure Prediction of Reservoir Fluids using Artificial Neural Network and Support Vector Machine
Bubble point pressure is an important parameter in equilibrium calculations of reservoir fluids and having other applications in reservoir engineering. In this work, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) have been used to predict the bubble point pressure of reservoir fluids. Also, the accuracy of the models have been compared to two-equation stat...
متن کاملPrediction of the pharmaceutical solubility in water and organic solvents via different soft computing models
Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificia...
متن کاملA Study of Earthquake mining using Support Vector Machine
— An Earthquake is more important for geophysics and economy problems. The Support Vector Machine of data mining techniques with cluster analysis is used to predict impact of earthquake [2]. The historical data are collected which has follow the time series methodology, combine the data mining for pre-processing and finally apply the SVM to predict the impact of earthquake. Earthquake predictio...
متن کامل