Machine learning is a part of artificial intelligence that concentrates on developing and designing methods to teach systems to be more intelligent and independent by learning from data. To exemplify, machine learning can be used to train systems to be able to distinguish between spam and non-spam messages. The core of machine learning deals with representation and generalization. Representation is about analyzing the data instances learning important properties useful for learning. Generalization is the ability of the system to perform the desired job well on unseen data instances. In another way, machine learning is all about building a model and training the system with training examples from unknown probability distribution to enable it to make accurate predictions on new instances. One of the approaches to achieve machine learning is Support Vector Machines (SVM). Support vector machines are supervised learning models based on learning algorithms to analyze data and recognize patterns used for classification and regression analysis.
Traditional training algorithms for SVM such as chunking and SVM capable of scaling super linearly with the number of examples becomes infeasible for large training sets. As dataset sizes are steadily growing over past few years, this necessitates the development of training algorithms that can handle large datasets. Large scale datasets are defined as datasets that cannot be stored in a modern computer’s memory. Large scale training algorithms use one of the following methods
SGD generalizes well even though it is poor at optimization. Popular algorithms that use SGD are PEGASOS and FOLOS.
In general, LSVM model is used for linear classifications but with the help of kernel trick can be applied to non-linear classification also. LSVM classifiers are used in Credit risk evaluation, text and hypertext categorization, classification of images, in medical sciences. For example, it classifies proteins up to 90% of the compounds and recognition of hand-written characters. In past few years, LSVM has become very prominent machine learning approach drawing much attention from many researchers and companies to invest huge amounts in developing better algorithms.
A large training set poses a challenge for the computational complexity of a learning algorithm demanding for more sophisticated computational equipment increasing the training cost drastically. Since a huge training cost is involved in large scale support vector machines (LVSM), small companies have to be very careful in making decisions to use LSVM. Lack of access to high computational equipment to small companies slows down the training process thereby, affecting the service time to the customers. Many researchers working on LSVM try to find an optimal solution to handle large datasets by applying different techniques to make it cost-effective and faster.
The training of LSVM algorithms requires enormous memory space and considerable computational time due to enormous amounts of training data and the non-linear programming problem. In general, most of the LSVM uses a random selection of training samples resulting in large training times to identify prominent properties of training sets. The main drawback in random selection is significant randomness that is involved in the training sets. On the other hand, randomness in sample data is important in improving the generalization capability of the algorithm, as it can be applied to a wide variety of data. Training algorithm with random training sets requires high computational capable equipment thereby, increasing training cost as well as total research cost. Apart from computational complexity involved it also requires a large number of training times resulting slowing services to clients (Cheng & Shih, 2007).
LSVM algorithm after training produces a number of support vectors which are used to deal with new datasets. The complexity of LSVM mainly depends on the number of support vectors. More the number of support vectors the better is the generalization of the SVM. On the other hand, processing time increases as more support vectors slow down decision speed and also affects accuracy. In some cases, companies have to use more than one LSVM algorithm to improve accuracy and generalization that increases operational cost. Generalization and accuracy are two contradicting terms that need to be balanced with great care without compromising on any one of them. An easy way to address this issue is the generation of support vectors depending on the data to be analyzed. This necessitates developing a better LSVM algorithm (Zhana & Shenb, 2005).
Testing LSVM becomes a challenge in last few years due to large size testing datasets and a large number of support vectors. The testing process is similar to training because it involves the selection of training sets and validating the LSVM. Initially when collecting samples for training sets one third is set aside for testing purpose. The results of LSVM over testing data are validated to know the generalization and accuracy.
Considering all the factors, it can be said that to have a cost-effective and faster LSVM a lot of improvement are required in training, algorithm and testing phases. The main purpose of this research is to improve training, algorithm and testing phases simultaneously.
The primary goal of this Research is to provide better and faster services to the clients as well as minimizing the cost for the company in training thereby, maintaining large scale support vector machines. This goal would be met through the following objectives
The scope of the research will include training samples, LSVM algorithm and testing phase. Algorithms related to other SVM are not included in this paper. LSVM implementation methods are also not discussed in this paper.
The proposed research has several benefits to SVM using companies and some of them are listed below
The deliverables from this research are listed below
The importance of Support Vector Machines is highly recognized in recent times. Though the roots of SVM concepts goes long back, it suggests incapable electronic technologies that restricted SVM implementation. Current advancements in electronics support implementation of SVM. However, the industries still strive to improve the process of SVM to make the services faster and cheaper.
The identified areas to be improved for overall effective performance of SVM are organized into following broad categories:
Table 1 shows a summary of the literature collected on the three categories as part of this research, and the collected sources are discussed briefly in the following sections.
Training SVM using large data sets is a kind of challenging task and time consuming. Effective and quicker training of SVM is essential for profitability as well as effectual performance. Rivas-Perea, Cota-Ruiz, & Rosiles (2013) proposes an algorithm to train large-scale Linear Programming Support Vector Machine(LP-SVM). This algorithm uses techniques like variable decomposition and constraint decomposition, which results in number of sub-LP-SVR structures. The resulting LP problems are solved sequentially. Cheng & Shih (2007) uses the active query in detecting substantially useful samples to train SVM rather than selecting samples randomly. The samples are detected based on their weights calculated from the confidence factor and their distance to the hyper plane. Zhou, Zhang, & Jiao (2002) propose an effective method to train the linear programming and non-linear programming SVMs by relaxing the constraints in order to reduce the dimension of the problem. This helps to quicken the training process with minimum generalization error. Zhan & Shen (2005) explains a training method to improve the efficiency of the SVM. Initially, support vectors are produced by training the SVM with all the training samples and then support vectors which have highly convoluted hypersurface are excluded from training set.
Efficient algorithm is very important for the better performance of SVM. Algorithm defines the character and capabilities of SVM. Ayat, Cheriet, & Suen (2005) proposes algorithm to reduce most common generalization error in SVM by reducing the number of support vectors. This paper presents the techniques to reduce the number of support vectors by optimizing the kernel parameters. Adankon & Cheriet (2007) proposes a method for approximation of the gradient of the empirical error, along with incremental learning, to reduce the resources required both in terms of processing time and of storage space. Yajima (2005) discuss transforming commonly used quadratic programming problems in SVM to linear programming problems. This paper also explains linear programming formulations for multicategory classification problems. Zhan & Shen (2006) explains the inclusion of an adaptive penalty term in the objective function to suppress the effect of outliers thereby simplifying the separation hypersurface and increasing the classification efficiency of SVM. Zhao & Sun (2009) proposes a recursive reduced least squares support vector regression. The main objective of the proposed algorithm is to reduce the number of support vectors without relaxing any constraints generated by the whole training set. This is achieved by generating support vectors using data which make more contribution to target function.
Improving testing phase
Unfortunately, SVM is currently considerably slower in test phase caused by number of the support vectors. Li, Jiao, & Hao (2007) uses an adaptive algorithm named feature vector selection (FVS) to select support vectors based on the vector correlation principle and greedy algorithm. This reduces the number of support vectors involved in testing phase.
Many techniques and methods are proposed in the reviewed papers in table 1 is used to increase the efficiency of the SVM. The authors made their effort to improve SVM by addressing issues separately in each of the identified potential areas that can determine the performance of SVM. There is no effort made by an author to improve all the areas simultaneously to have a better SVM than the existing ones. The main objective of the proposed research is to develop an algorithm to simultaneously improve training, efficiency of algorithm and testing phase of SVM in order to reduce the cost involved and for better client service.
Research plan is essential in order to guide both research execution and research control. The main elements of research plan are to define research methodology for the research problem, to schedule tasks and milestones for the research, and identifying the resources based on the research methodology. The following sub-sections describe the elements of research plan.
To achieve research objectives an appropriate methodology must be identified depending upon the research problems. The training problem in large scale support vector machines (LSVM) can be addressed by using the algorithms implementing variable decomposition and constraint decomposition (Rivas-Perea, Cota-Ruiz, & Rosiles, 2013). Variable decomposition and constraint decomposition results in sub-LP-SVM structures that are solved sequentially. The implementation of this algorithm evades the necessity to use high-end computational resources and also reduces training time.
The objective of improving generalization can be achieved by reducing the number of support vectors. The number of support vectors can be reduced by optimizing the kernel parameters (Ayat, Cheriet, & Suen, 2005). In this methodology, support vectors are generated only after analysing the data sets, which is unlikely in conventional algorithms, thus increasing the accuracy of the algorithm. A vector correlation principle and greedy algorithm are also included in the algorithm to further reduce the support vectors involved in the testing phase. Generalization can also be increased by applying techniques like using an adaptive penalty term in objective function or by recursive reduced least squares support vector regression but reducing the number of support vectors by proposed methodology has the advantage of being cost effective and quicker.
The proposed research will require the following resources to complete the research fully and in a timely manner.
The research will also require an available data generation system to get the potential training data and testing data.
The analysis involves mainly quantitative analysis. Quantitative analysis involves exploratory data analysis, visualizations to show the performance trends of the different versions of the algorithm and to measure the key metrics like train and test data preparation time. All the improvement techniques performance is measured against a standard data set.
The tasks that will be pursued to implement the proposed methodology are listed below. Some tasks may proceed in parallel and some sequentially.
Task 1: Analyze research needs and define requirements
1.1 Conduct needs analysis and identifies Support Vector Machine (SVM) efficiency needs.
1.2 Define research objectives and scope.
1.3 Define research requirements.
Task 2; Plan for the research
2.1 Develop a detailed research plan and establish milestones.
2.2 Identify resources needed and obtain the resources.
Task 3: Review literature related to the SVM efficiency
3.1 Identify literature sources related to the SVM efficiency
3.2 Collect relevant literature and analyze it.
3.3 Identify information from the literature applicable to improve SVM efficiency.
Task 4: Collect and analyze current SVM training and testing data.
4.1 Collect sample data that are used to train and test LSVM.
4.2 Analyze the sample data to identify the vital and necessary information required to develop training and testing methods.
Task 5: Design the LSVM testing method, algorithm and testing phase
5.1 Design a training method of the system.
5.2 Design an algorithm of the system.
5.3 Design a testing method of the system.
5.4 Develop a preliminary design of the LSVM.
5.5 Validate the training and algorithm design of the LSVM.
Task 6: Develop the LSVM model
6.1 Identify the software platform to develop the LSVM.
6.2 Develop testing method, algorithm and testing phase of LSVM according to the design architecture.
Task 7: Train, test and evaluate efficiency of LSVM
7.1 Train the LSVM using training data.
7.2 Test the LSVM using the testing data.
7.3 Evaluate the efficiency of LSVM by comparing training time, testing time and accuracy with other LSVMs.
Task 8: Implement the LSVM
8.1 Install the LSVM in the client’s area.
8.2 Test the system with users’ help and refine it further.
Task 9: Document the research and results
9.1 Document the details of the implemented system as a report.
9.2 Present the research and submit the report.
Figure1 shows the proposed research schedule including time line and milestones.
Figure 1. Research Timeline and Milestones
References
Adankon, M., & Cheriet, M. (2007). Optimizing resources in model selection for support vector machine. Pattern Recognition, 40, 953 – 963.
Ayat, N., Cheriet, M., & Suen, C. (2005). Automatic model selection for the optimization of SVM kernels. Pattern Recognition, 38, 1733 – 1745.
Cheng, S., & Shih, F. (2007). An improved incremental training algorithm for support vector machines using active query. Pattern Recognition, 40, 964 – 971.
Li, Q., Jiao, L., & Hao, Y. (2007). Adaptive simplification of solution for support vector machine. Pattern Recognition, 40, 972 – 980.
Rivas-Perea, P., Cota-Ruiz, J., & Rosiles, J.-G. (2013). An Algorithm for Training a Large Scale Support Vector Machine for Regression based on Linear Programming and Decomposition Methods. Pattern Recognition, 34(4), 439–451.
Yajima, Y. (2005). Linear programming approaches for multicategory support vector machines. European Journal of Operational Research, 162, 514–531.
Zhan, Y., & Shen, D. (2005). Design efficient support vector machine for fast classification. Pattern Recognition, 38, 157 – 161.
Zhan, Y., & Shen, D. (2006). An adaptive error penalization method for training an efficient and generalized SVM. Pattern Recognition, 39, 342 – 350.
Zhao, Y., & Sun, J. (2009). Recursive reduced least squares support vector regression. Pattern Recognition, 42, 837–842.
Zhou, W., Zhang, L., & Jiao, L. (2002). Linear programming support vector machines.Pattern Recognition, 35, 2927 – 2936.
Agus, W., & Samingun, H. (2017). The Classification Performance using Logistics Rgression And Support Vector Machine (SVM). Journal of Theoretical & Applied Information Technology, 5184-5193.
Baykal, C., Liebenwein, L., & Schwarting, W. (2017). Data Structures and Algorithms. Retrieved from Cornell University Library: https://arxiv.org/abs/1708.03835
Hsu, C.-W., Chang,, C.-C., & Lin, C.-J. (2016). A Practical Guide to Support Vector Classi. Retrieved from https://www.csie.ntu.edu.tw/~cjlin
JinXing, C., YouLong, Y., Li, L., YanYing, L., & SuLing, Z. (2017,). A modified support vector regression: Integrated selection of training subset and model. Applied Soft Computing, 308-322.
Matteo, F. (2016). Fast training of Support Vector Machines with Gaussian kernel. Discrete Optimization, 183-194.
Pereira, L., Papa, J., & de Souza, A. (2013). Harmony search applied for support vector machines training optimization. IEEE, 998-1002.
Sebastian, P., Nassir, N., & Amin, K. (2016). An Efficient Training Algorithm for Kernel Survival Support Vector Machines. Retrieved from arXiv: https://arxiv.org/abs/1611.07054
Ustuner, M., Sanli, F., & Abdikan, S. (2016). Balanced VS Imbalanced training data: Classifying Rapideye data with Support Vector Machines. Copernicus Publications.
Vanek, J., Michalek, J., & Psutka, J. (2017). A GPU-Architecture Optimized Hierarchical Decomposition Algorithm for Support Vector Machine Training. IEEE Transactions on Parallel & Distributed Systems, 3330-3343.
Verbiest, N., Derrac, J., Cornelis, C., García, S., & Herrera, F. (2016). Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis. Applied Soft Computing, 10-22.
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