Boyang Li

A fast SVM training method for very large datasets

In a standard support vector machine (SVM), the training process has O(n3) time and O(n2) space complexities, where n is the size of training dataset. Thus, it is computationally infeasible for very large datasets. Reducing the size of training dataset is naturally considered to solve this problem. SVM classifiers depend on only support vectors (SVs) that lie close to the separation boundary. Therefore, we need to reserve the samples that are likely to be SVs. In this paper, we propose a method based on the edge detection technique to detect these samples. To preserve the entire distribution properties, we also use a clustering algorithm such as K-means to calculate the centroids of clusters. The samples selected by edge detector and the centroids of clusters are used to reconstruct the training dataset. The reconstructed training dataset with a smaller size makes the training process much faster, but without degrading the classification accuracies.

Support Vector Machine with Fuzzy Decision-Making for Real-world Data Classification

This paper proposes an improved model for the application of support vector machine (SVM) to achieve the real-world data classification. Being different from traditional SVM classifiers, the new model takes the thought about fuzzy theory into account. And a fuzzy decision-making function is also built to replace the sign function in the prediction stage of classification process. In the prediction part, the method proposed uses the decision value as the independent variable of fuzzy decision-making function to classify test data set into different classes, but not only the sign of which. This flexible design of decision-making model more approaches to the properties of real-world conditions in which interaction and noise influence exist around the boundary between different clusters. So many misclassified cases can be modified when these sets are considered as fuzzy ones. In addition, a boundary offset is also introduced to modify the excursion produced by the imbalance of real-world dataset. Then an improved and more robust performance will be presented by using this adjustable fuzzy decision-making SVM model in simulations.

Feature selection for Human resource selection based on Affinity Propagation and SVM sensitivity analysis

Feature selection is a process to select a subset of original features. It can improve the efficiency and accuracy by removing redundant and irrelevant terms. Feature selection is commonly used in machine learning, and has been wildly applied in many fields. we propose a new feature selection method. This is an integrative hybrid method. It first uses Affinity Propagation and SVM sensitivity analysis to generate feature subset, and then use forward selection and backward elimination method to optimize the feature subset based on feature ranking. Besides, we apply this feature selection method to solve a new problem, Human resource selection. The data is acquired by questionnaire survey. The simulation results show that the proposed feature selection method is effective, it not only reduced human resource features but also increased the classification performance.

Financial time series prediction using a support vector regression network

This paper presents a novel support vector regression (SVR) network for financial time series prediction. The SVR network consists of two layers of SVR: transformation layer and prediction layer. The SVRs in the transformation layer forms a modular network; but distinguished with conventional modular networks, the partition of the SVR modular network is based on the output domain that has much smaller dimension. Then the transformed outputs from the transformation layer are used as the inputs for the SVR in prediction layer. The whole SVR network gives an online prediction of financial time series. Simulation results on the prediction of currency exchange rate between US dollar and Japanese Yen show the feasibility and the effectiveness of the proposed method.

Gene classification using an improved SVM classifier with soft decision boundary

One of the central problems of functional genomics is gene classification. Microarray data are currently a major source of information about the functionality of genes. Various mathematical techniques, such as neural networks (NNs), self-organizing map (SOM) and several statistical methods, have been applied to classify the data in attempts to extract the underlying knowledge. As for conventional classification, the problem mainly addressed so far has been how to classify the multi-label gene data and how to deal with the imbalance problem. In this paper, we proposed an improved support vector machine (SVM) classifier with soft decision boundary. This boundary is a classification boundary based on belief degrees of data. The boundary can reflect the distribution of data, especially in the mutual part between classes and the excursion caused by the data imbalance.

Fuzzy Decision-making SVM with An Offset for Real-world Lopsided Data Classification

An improved support vector machine (SVM) classifier model for classifying the real-world lopsided data is proposed. The most obvious differences between the model proposed and conventional SVM classifiers are the designs of decision-making functions and the introduction of an offset parameter. With considering about the vagueness of the real-world data sets, a fuzzy decision-making function is designed to take the place of the traditional sign function in the prediction part of SVM classifier. Because of the existence of the interaction and noises influence around the boundary between different clusters, this flexible design of decision-making model which is more similar to the real-world situations can present better performances. In addition, in this paper we mainly discuss an offset parameter introduced to modify the boundary excursion caused by the imbalance of the real-world datasets. Because noises in the real-world can also influence the separation boundary, a weighted harmonic mean (WHM) method is used to modify the offset parameter. Due to these improvements, more robust performances are presented in our simulations

Human resource selection based on performance classification using weighted support vector machine

Recruitment and selection have the first priority in human resource management. Traditional methods is based on linear model, it selects candidate by appraisal and ranking. However, selection system is intricate and nonlinear. Ranking method can not find the right person sometimes. For a ranked candidate, the job performance after being selected is unclear. The recruitment is unsuccessful when the candidate can not perform the expected job performance. In order to find the right person for the right position, this paper proposes a selection system using support vector machine (SVM). This system is fit for the nonlinear problem, and it gives a prediction of job performance. Considering the character of human resource selection problem, a weighted method based on weighted support vector machine (WSVM) is proposed. This method can improve the performance of traditional SVM. Technique of scaling, expert judgment, simple additive weighted, analytic hierarchy process (AHP) and questionnaire survey were used in this study. Simulation results show that classification system based on SVM is valid for human resource selection; furthermore, WSVM performs a better efficiency than traditional SVM for job performance classification. This proposed system can be used to support the decision of human resource selection.