Granular Support Vector Machine Research Articles

Crop Yield Prediction using Granular SVM

Agriculture is the backbone of the Indian economy. Farming is a major source of income for many people in developing countries. Prediction of yield of crops is desirable as it can predict the income and minimise losses for the farmersunder unfavorable conditions. But predicting crop yield is a challenging task in developing countries like India. Conventionally, crop yield prediction is done using farmer’s expertise. The sustainability and productivity of a crop growing area are dependent on suitable climatic, soil, and biological conditions. So, data mining techniques based on neural networks, Neuro-Fuzzy Inference Systems, Fuzzy Logic, SMO, and Multi Linear Regression can be used for prediction. Previous work has performed yield prediction based on crop models considering only some of the environmental factors. This work uses a Support Vector Machine (SVM) to predict the crop yield under different environmental conditions that include soil, climate, and biological factors. Applying granular computing enables dividing the problem space into a sequence of subtasks. So, the hyperplane construction of SVM can be parallelized by splitting the problem space. Testing can also be parallelized. The main advantage is that linear SVM can be used to handle higher dimension space. Time complexity is reduced. Prediction using granular SVM can be parallelized using appropriate techniques like MapReduce/GPGPU. IoT-based agriculture increases crop yield by accurate prediction, automation, remote monitoring, and reducing wastage of resources. IoT-based monitoring systems can be used by farmers, researchers, and government officials to analyze crop environments and statistical information to predict crop yield. This paper proposes an IoT-based system to predict crop yield based on climatic, soil, and biological factors using parallelized granular support vector machines.

Prediction of phosphorylation sites based on granular support vector machine

Protein phosphorylation is the most extensive and important post-translational modification in eukaryotes, regulating the activity of almost all cells. Experimental methods used to identify phosphorylation sites, such as mass spectrometry, are costly and time-consuming. A number of algorithms have been developed to predict phosphorylation sites. However, they often select small data volume by random sampling. This cannot make full use of the characteristics of the entire data set to build a prediction model. According to the granularity calculation combined with the kernel fuzzy C-means clustering, this paper maps the massive raw data to a high-dimensional kernel space, and then divides the grains by clustering to obtain high-dimensional equilibrium grains. In particular, a specific granular support vector machine (KFCC–GSVM) prediction model is built in equilibrium grain data. This novel model improves the rationality and reliability of phosphorylation site data compression, so that the compressed data has the same distribution in the kernel space as the pre-compression data when applying the traditional SVM algorithm classification. Experimental results demonstrate that our method is better than the SVM-based non-kinase-specific phosphorylation site prediction method—Musite and the traditional GSVM method.

Granular support vector machine: a review

The time complexity of traditional support vector machine (SVM) is $$O(l^{3})$$ and l is the the training sample size, and it can not solve the large scale problems. Granular support vector machine (GSVM) is a novel machine learning model based on granular computing and statistical learning theory, and it can solve the low efficiency learning problem that exists in the traditional SVM and obtain satisfactory generalization performance, as well. This paper primarily reviews the past (rudiment), present (basic model) and future (development direction) of GSVM. Firstly, we briefly introduce the basic theory of SVM and GSVM. Secondly, we describe the past related research works conducted before the GSVM was proposed. Next, the latest thoughts, models, algorithms and applications of GSVM are described. Finally, we note the research and development prospects of GSVM.

Support vector machine based on hierarchical and dynamical granulation

Support vector machine (SVM) has been a promising method for classification and regression areas due to its solid statistical foundations, such as margin maximization and kernel methods. However, SVM is not typically used for large-scale data mining problems because its training complexity is highly dependent on the dataset size. This paper presents an improved granular support vector machine learning model based on hierarchical and dynamical granulation, namely, HD_GSVM, to solve the low learning efficiency and generalization performance problem of traditional granular support vector machines (GSVM). For HD_GSVM, the original data will be mapped into a high-dimensional space by a Mercer kernel. Then, the data are divided into several granules, and those granules near the approximate hyperplane are extracted and re-granulated on a subtle level by their density and radius degree. Finally, the decision hyperplane will be obtained through all of the granules at different hierarchical and dynamical granulation levels effectively. During the granulation process, the granulation level of all granules can be dynamically changed continuously. With this method, different classification information can be obtained from different levels of granules; to meet a variety of needs for various practical problems from different perspectives. The experimental results on the UCI benchmark datasets demonstrate that the proposed HD_GSVM model can improve the generalization performance greatly with high efficiency synchronously.

Granular support vector machine to identify unknown structural classes of protein.

To date, classification of structural class using local protein structure rather than the whole structure has been gaining widespread attention. It is noted that the structural class lies in local composition or arrangement of secondary structure, while the threshold-based classification method has restricted rules in determining these structural classes. As a consequence, some of the structures are unknown. In order to determine these unknown structural classes, we propose a fusion algorithm, abbreviated as GSVM-SigLpsSCPred (Granular Support Vector Machine--with Significant Local protein structure for Structural Class Prediction), which consists of two major components, which are: optimal local protein structure to represent the feature vector and granular support vector machine to predict the unknown structural classes. The results highlight the performance of GSVM-SigLpsSCPred as an alternative computational method for low-identity sequences.

A novel granular support vector machine based on fuzzy kernel clustering

For the traditional granular support vector machine (GSVM), the training samples are granulated in the original space and then are mapped into the kernel space. However, this method will lead to the inconsistent distribution of the data between original space and kernel space, thereby reducing the generalisation of GSVM. To solve this problem, a method called granular support vector machine based on fuzzy kernel cluster is proposed. This method directly granulates the training data and selects support vector particles in kernel space, and then trains the support vector particles in the same kernel space by GSVM. Finally, the experiments on benchmark datasets demonstrate the effectiveness of the proposed approach.

Dynamical Granular Support Vector Regression Machine

PDF HTML阅读 XML下载 导出引用 引用提醒 动态粒度支持向量回归机 DOI: 10.3724/SP.J.1001.2013.04472 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(60975035,61273291);山西省回国留学人员科研基金(2012008);山西省研究生教育创新项目(20133001) Dynamical Granular Support Vector Regression Machine Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:粒度支持向量机(granular support vector machine,简称GSVM)可以有效提高支持向量机(support vectormachine,简称SVM)的学习效率,但由于经典GSVM 通常将粒用个别样本替代,且粒划和学习在不同空间进行,因而不可避免地改变了原始数据分布,从而可能导致泛化能力降低.针对这一问题,通过引入动态层次粒划的方法,设计了动态粒度支持向量回归(dynamical granular support vector regression,简称DGSVR)模型.该方法首先将训练样本映射到高维空间,使得在低维样本空间无法直接得到的分布信息显示出来,并在该特征空间中进行初始粒划.然后,通过衡量样本粒与当前回归超平面的距离,找到含有较多回归信息的粒,并通过计算其半径和密度进行深层次的动态粒划.如此循环迭代,直到没有信息粒需要进行深层粒划时为止.最后,通过动态粒划过程得到的不同层次的粒进行回归训练,在有效压缩训练集的同时,尽可能地使含有重要信息的样本在最终训练集中保留下来.在基准函数数据集及UCI 上的回归数据集上的实验结果表明,DGSVR 方法能够以较快的速度完成动态粒划的过程并收敛,在保持较高训练效率的同时可有效提高传统粒度支持向量回归机(granular support vector regression machine,简称GSVR)的泛化性能. Abstract:Although granular support vector machine (GSVM) can improve the learning speed, the generalization performance may be decreased because the original data distribution will be changed inevitably by two reasons: (1) A granule is usually replaced by individual datum; (2) Granulation and learning are carried out in different spaces. To address this problem, this study presents a granular support vector regression (SVR) model based on dynamical granulation, namely DGSVR, by using the dynamical hierarchical granulation method. With DGSVR, the original data are mapped into the high-dimensional space by mercer kernel to reveal the distribution features implicit in original sample space, and the data are divided into some granules initially. Then, some granules are obtained with important regression information by measuring the distances of granules and regression hyperplane. By computing the radius and density of granules, the deep dynamical granulation process executes until there are no informational granules need to be granulated. Finally, those granules in different granulation levels are extracted and trained by SVR. The experimental results on benchmark function datasets and UCI regression datasets demonstrate that the DGSVR model can quickly finish the dynamical granulation process and is convergent. It concludes this model can improve the generalization performance and achieve high learning efficiency at the same time. 参考文献 相似文献 引证文献

A Novel Granular Support Vector Machine Based on Mixed Kernel Function

(1) School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China; (2) Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China; (3) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China; (4) Geomatics College Shandong University of Science and Technology Qingdao 266510 China

Granular support vector machine based on mixed measure

This paper presents a granular support vector machine learning model based on mixed measure, namely M_GSVM, to solve the model error problem produced by mapping, simplifying, granulating or substituting of data for traditional granular support vector machines (GSVM). For M_GSVM, the original data will be mapped into the high-dimensional space by mercer kernel. Then, the data are divided into some granules, and those mixed granules including more information are extracted and trained by support vector machine (SVM). Finally, the decision hyperplane will be corrected through geometric analyzing to reduced model error effectively. The experiment results on UCI benchmark datasets and Interacting Proteins database demonstrate that the proposed M_GSVM model can improve the generalization performance greatly with high learning efficiency synchronously.

Research of granular support vector machine

Granular support vector machine (GSVM) is a new learning model based on Granular Computing and Statistical Learning Theory. Compared with the traditional SVM, GSVM improves the generalization ability and learning efficiency to a large extent. This paper mainly reviews the research progress of GSVM. Firstly, it analyzes the basic theory and the algorithm thought of GSVM, then tracking describes the research progress of GSVM including the learning model and specific applications in recent years, finally points out the research and development prospects.

Recursive Fuzzy Granulation for Gene Subsets Extraction and Cancer Classification

A typical microarray gene expression dataset is usually both extremely sparse and imbalanced. To select multiple highly informative gene subsets for cancer classification and diagnosis, a new Fuzzy Granular Support Vector Machine---Recursive Feature Elimination algorithm (FGSVM-RFE) is designed in this paper. As a hybrid algorithm of statistical learning, fuzzy clustering, and granular computing, the FGSVM-RFE separately eliminates irrelevant, redundant, or noisy genes in different granules at different stages and selects highly informative genes with potentially different biological functions in balance. Empirical studies on three public datasets demonstrate that the FGSVM-RFE outperforms state-of-the-art approaches. Moreover, the FGSVM-RFE can extract multiple gene subsets on each of which a classifier can be modeled with 100% accuracy. Specifically, the independent testing accuracy for the prostate cancer dataset is significantly improved. The previous best result is 86% with 16 genes and our best result is 100% with only eight genes. The identified genes are annotated by Onto-Express to be biologically meaningful.