Feature Weighted Support Vector Machine Research Articles

An IPSO-FW-WSVM Method for Stock Trading Signal Forecasting

Trading signal detection is a very popular yet challenging research topic in the financial investment area. This paper develops a novel method integrating piecewise linear representation (PLR), improved particle swarm optimization (IPSO) and a feature-weighted support vector machine (FW-WSVM) to analyze the nonlinear relationships between trading signals and the stock data hidden in historical data. First, PLR is applied to generate numerous trading points (valleys or peaks) based on the historical data. These turning points' prediction is formulated as a three-class classification problem. Then, IPSO is utilized to find the optimal parameters of FW-WSVM. Lastly, we conduct a series of comparative experiments between IPSO-FW-WSVM and PLR-ANN on 25 stocks with 2 different investment strategies. The experiment results show that our proposed method achieves higher prediction accuracy and profitability, which indicates the IPSO-FW-WSVM method is effective in the prediction of trading signals.

A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction

Academic performance, a globally understood metric, is utilized worldwide across disparate teaching and learning environments and is regarded as a quantifiable indicator of learning gain. The ability to reliably estimate student’s academic performance is important and can assist academic staff to improve the provision of support. However, it is recognized that academic performance estimation is non-trivial and affected by multiple factors, including a student’s engagement with learning activities and their social, geographic, and demographic characteristics. This paper investigates the opportunity to develop reliable models for predicting student performance using Artificial Intelligence. Specifically, we propose two-step academic performance prediction using feature weighted support vector machine and artificial neural network (ANN) learning. A feature weighted SVM, where the importance of different features to the outcome is calculated using information gain ratios, is employed to perform coarse-grained binary classification (pass, $$P1$$ , or fail, $$P0$$ ). Subsequently, detailed score levels are divided from D to A+, and ANN learning is employed for fine-grained, multi-class training of the $$P1$$ and $$P0$$ classes separately. The experiments and our subsequent ablation study, which are conducted on the student datasets from two Portuguese secondary schools, have proved the effectiveness of this hybridized method.

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.

In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, nonlinear, and susceptible to interference. As a feature extraction method for motor imagery, Common Spatial Pattern (CSP) has been proven to be very effective. However, its effectiveness depends heavily on the choice of frequency bands, and Euclidean space cannot effectively describe the inner relationship. To solve these problems, a classification approach for motor imagery using the Geodesic Filtering Common Spatial Pattern (GFCSP) and filter-bank Feature Weighted Support Vector Machine (FWSVM) is presented. First, GFCSP based on the Riemannian manifold is proposed, in which the extracted covariance features are spatially filtered in Riemannian tangent space, and the average covariance matrix is replaced by Riemannian mean in CSP. Second, filter-bank FWSVM with a feature weighted matrix is proposed. EEG signals are filtered into 8-12 Hz, 12-16 Hz, 18-22 Hz, 22-26 Hz, and a wide band of 8-24 Hz, and GFCSP features of these filtered signals are extracted. A feature weighted matrix is calculated using mutual information and the Pearson correlation coefficient from these features and class information. Then, the Support Vector Machine (SVM) is used for classification with the feature weighted matrix. Finally, the proposed method is validated on the dataset IVa in BCI competition III. Classification accuracies of the five subjects are 92.31%, 99.03%, 80.36%, 96.30%, and 97.67%, which demonstrate the effectiveness of our proposed method.

A novel framework for stock trading signals forecasting

This study investigates stock trading points prediction that is an attractive yet challenging research topic in the financial investment area, as the stock market is an unstable and complex system. A small improvement in the predictive performance can make profit. To realize trading points detection, we propose a novel method which integrates piecewise linear representation (PLR) and feature weighted support vector machine (FW-WSVM) to forecast the stock trading points (PLR–FW-WSVM). Firstly, we generate numerous trading points (valley or peak) from the trading data by PLR and formulate the stock trading points prediction as a weighted four-class classification problem. Then, we estimate the importance of each input feature by computing the information gain and apply FW-WSVM to learn the prediction model between the trading points and the input features from the historical data. Afterward, the model is used to forecast the future turning points from the input features. Lastly, we conduct a series of experiments among PLR–FW-WSVM, PLR–WSVM and PLR–ANN over 30 stocks with different investment strategies. The results show that our proposed method generates the highest accuracy and profits in average, which indicates PLR–FW-WSVM is effective and can be applied to forecast the future trading points in the real-world application.

A locally weighted KNN algorithm based on eigenvector of SVM

K-Nearest Neighbours (KNN) is one of the fundamental classification methods in machine learning. The performance of KNN method is restricted by the number of neighbours k. It is obvious that the outliers appear when dealing with small data samples. In this paper, we propose a hybrid framework of the feature weighted support vector machine as well as locally weighted k-nearest neighbour (SLKNN) to overcome this problem. In our method, we first use support vector machine to calculate the eigenvector of feature of data, then apply this eigenvector into distance metric as the weight of the feature. Finally, the distance metric is used in locally weighted k-nearest neighbour. The experiments on UCI data sets show that the proposed SLKNN performs better than some KNN-based methods.

Exploration and evaluation of individual difference to driving fatigue for high‐speed railway: a parametric SVM model based on multidimensional visual cue

High-speed rails have a significantly higher driving safety requirement than other public transport because of more passengers and faster speed. However, the particularity of train driving often leads to railway drivers more susceptible to drowsiness and fatigue, and this fatigue has a distinct personality. Here, the authors analyse the mechanism of individual fatigue generation of different drivers, and use eye movement data collected by non-contact means as an objective measurement, and combine subjective sleepiness assessment to reveal the existence of individual differences in fatigue in high-speed railway driving. Furthermore, a novel driver-specific feature weighted support vector machine (FWSVM) algorithm is proposed to handle the individual differences. In the FWSVM, features are assigned with different weights by the information gain to reflect classification importance and individual effects. The average accuracy of FWSVM is 90.98%, the average sensitivity is 92.01%, and the average specificity is 89.88%, which is better than the classical SVM. Such improvements are attributed to the quantitative evaluation of individual effects by the weighted features. These results can be used as a preliminary study to design a high-speed rail vehicle interface to prevent driver fatigue.

A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

This study investigates stock market indices prediction that is an interesting and important research in the areas of investment and applications, as it can get more profits and returns at lower risk rate with effective exchange strategies. To realize accurate prediction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. In this paper, we propose a basic hybridized framework of the feature weighted support vector machine as well as feature weighted K-nearest neighbor to effectively predict stock market indices. We first establish a detailed theory of feature weighted SVM for the data classification assigning different weights for different features with respect to the classification importance. Then, to get the weights, we estimate the importance of each feature by computing the information gain. Lastly, we use feature weighted K-nearest neighbor to predict future stock market indices by computing k weighted nearest neighbors from the historical dataset. Experiment results on two well known Chinese stock market indices like Shanghai and Shenzhen stock exchange indices are finally presented to test the performance of our established model. With our proposed model, it can achieve a better prediction capability to Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index in the short, medium and long term respectively. The proposed algorithm can also be adapted to other stock market indices prediction.

The crisis early warning of the quality of supply chain based on rough set&feature weighted support vector machine

A Rough Set&Feature Weighted Support Vector Machine(RS-FWSVM) model is proposed for the quality of supply chain crisis early-warning, which aims at some problems of the quality of supply chain. This model combines the advantages of the RS and FWSVM, which can get classification per-formances by changing the weights of different linear functions in the feature space. Application process of this model to the crisis early warning of SCQ is researched, which can help enable chain enterprises to identify crises in the process of operations and to predict possible crises.

Denoising of EEG signals using Discrete Wavelet Transform based Scalar Quantization

Efficient transmission of EEG signals is necessary to optimize the performance of Brain Computer Interface (BCI) applications. This can be achieved by improving the signal to noise ratio of EEG signals. There are various methods employed in denoising such signals. Since Bio medical signals contains more redundancies it is also necessary to compress the EEG signals. For that quantization based compression schemes are used which includes complexity in the circuit. Hence Discrete Wavelet transform based Scalar Quantization (DWTSQ) is used for improving the Signal to Noise Ratio (SNR) with reduced complexity has been proposed in this project. The hybridization of quantizer with DWT transform reduces number of bits needed for storing the transmitted coefficients. The Denoised signal is classified using Immune Feature Weighted Support Vector Machine(IFWSVM) for measuring the accuracy of EEG features.. This DWTSQ increases the SNR of the transmitted signal and also produces better accuracy for different features. This is simulated using MATLAB software which gives better result than other techniques used today.

Credit scoring by feature-weighted support vector machines

Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, a novel feature-weighted support vector machine (SVM) credit scoring model is presented for credit risk assessment, in which an F-score is adopted for feature importance ranking. Considering the mutual interaction among modeling features, random forest is further introduced for relative feature importance measurement. These two feature-weighted versions of SVM are tested against the traditional SVM on two real-world datasets and the research results reveal the validity of the proposed method.

Application of feature-weighted Support Vector regression using grey correlation degree to stock price forecasting

A feature-weighted Support Vector Machine regression algorithm is introduced in this paper. We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value. However, this assumption is not always true in real problems. In the proposed new algorithm, we give different weight values to different features of the samples in order to improve the performance of SVM. In our algorithm, firstly, a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem, and then the values of the grey correlation degree are used as weight values assigned to the features. The proposed method is tested on sample stock data sets selected from China Shenzhen A-share market. The result shows that the new version of SVM can improve the accuracy of the prediction.

Linear Feature-weighted Support Vector Machine

The existing support vector machines (SVMs) are all assumed that all the features of training samples have equal contributions to construct the optimal separating hyperplane. However, for a certain real-world data set, some features of it may possess more relevances to the classification information, while others may have less relevances. In this paper, the linear feature-weighted support vector machine (LFWSVM) is proposed to deal with the problem. Two phases are employed to construct the proposed model. First, the mutual information (MI) based approach is used to assign appropriate weights for each feature of the whole given data set. Second, the proposed model is trained by the samples with their features weighted by the obtained feature weight vector. Meanwhile, the feature weights are embedded in the quadratic programming through detailed theoretical deduction to obtain the dual solution to the original optimization problem. Although the calculation of feature weights may add an extra computational cost, the proposed model generally exhibits better generalization performance over the traditional support vector machine (SVM) with linear kernel function. Experimental results upon one synthetic data set and several benchmark data sets confirm the benefits in using the proposed method. Moreover, it is also shown in experiments that the proposed MI based approach to determining feature weights is superior to the other two mostly used methods.