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

Chen, Yingjun; Hao, Yong

doi:10.1016/j.eswa.2017.02.044

Cited by 287 publications

(136 citation statements)

References 47 publications

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“…In addition, attribute weighting is also performed on research work by [1]. in his research suggested the method of Information Gain as a basis for weighting attributes on the KNN algorithm known as Feature Weight K-Nearest Neighbor (FWKNN).…”

Section: Previous Researchmentioning

confidence: 99%

“…The solution to this problem is to give weight to each attribute [8]. In this research, we propose attribute weighting based K-Nearest Neighbor using Gain Ratio to increase the accuracy value of K-Nearest Neighbor (KNN) method by giving weight to each attribute, whereby the weights obtained are calculated using the normalization equation min-max, then calculatedthe data similarity using the distance model proposed by [1]. The rest of this paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 3 we will provide the result and discussions and in Section 4 will provide with conclusions 2. Feature Weight K-Nearest Neighbor (FWKNN) K-Nearest Neighbor (KNN) is a nonparametric learning method and sensitive to distance function due to the inherent sensitivity of irrelevant attributes [1]. So to get FWKNN modeling based on attribute weighting.FWKNN determines the weight of the attribute by identifying the nearest k-neighbor that has the highest similarity with the test data.…”

Section: Introductionmentioning

confidence: 99%

“…To measure the distance between data, one of the most popular options is the Euclidean distance model [1]. FWKNN uses a modified distance using the weighted feature for standard Euclidean distance expansion.…”

Section: Introductionmentioning

confidence: 99%

“…K-Nearest Neighbor (KNN) is one of the effective, simple and performs well method for classification [1][2][3], but from several studies, the result performance accuracy of KNN is lower than other methods. One of them is in the study by [4] which compared performance between support vector machine (SVM) and KNN.…”

Section: Introductionmentioning

confidence: 99%

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Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Nababan¹,

Sitompul

Tulus

2018

J. Phys.: Conf. Ser.

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Abstract. K-Nearest Neighbor (KNN) is a good classifier, but fro m several studies, the result performance accuracy of KNN still lo wer than other methods. One of the causes of the low accuracy produced, because each attribute has the same effect on the classification process, while some less relevant characteristics lead to mis s-classification of the class assignment for new data. In this research, we proposedAttribute Weighting Based K-Nearest Neighbor Using Gain Rat io as a parameter to see the correlation between each attribute in the data and the Gain Ratio also will be used as the basis for weighting each attribute of the dataset. The accuracy of results is compared to the accuracy acquired fro m the original KNN method using 10-fo ld Cross-Validation with several datasets from the UCI Machine Learning repository and KEELDataset Repository, such as abalone, glass identification,haberman, hayes -roth and water quality status. Based on the result of the test, the proposed method was able to increase the classification accuracy of KNN, where the highest difference of accuracy obtained hayes-roth dataset is worth 12.73%, and the lowest difference of accuracy obtained in the abalone dataset of 0.07%. The average result of the accuracy of all dataset increases the accuracy by 5.33%. (KNN) is one of the effective, simple and performs well method for classification [1][2][3], but from several studies, the result performance accuracy of KNN is lower than other methods. One of them is in the study by [4] which compared performance between support vector machine (SVM) and KNN. The result of their research seen that performance of SVM better than KNN, where the value accuracy obtained by SVM of 82.54% while the value obtained from KNN of 79.22%. Another study by [5] compared K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The results of their study seen that the performance of ANN (with 5 hidden layers) better than KNN, where the result of accuracy is 90.5%. Research by [6] compared the performance of Naïve Bayes, Decision Tree and K-Nearest Neighbor (KNN). The results show that Naïve Bayes has the best accuracy in classification compared to Decision Tree Tree and K-Nearest Neighbor (KNN) with an average accuracy of 73.7%. whilethe average accuracy of Decision Tree and KNN respectively 58.9% and 56.7%. In the study by [7] compared KNN and Naïve Bayes in diagnosing heart disease. Results obtained by the value of accuracy Naïve Bayes of 79.62% while the average value of accuracy on KNN is only 64.85% whenk = 10. Introduction K-Nearest Neighbor

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Section: Previous Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Nababan¹,

Sitompul

Tulus

2018

J. Phys.: Conf. Ser.

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Stock Market Predictions Using Deep Learning

Sharma,

Mehta

2024

Deep Learning Tools for Predicting Stock Market Movements

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Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model

Zhou,

Zhu

2024

Journal of Forecasting

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Because the exchange rate is essentially a dynamic and nonlinear system, exchange rate forecasting has been one of the most challenging topics in the financial field. This paper proposes a novel idea of “decomposition‐reconstruction‐integration” to predict exchange rate. First, based on ICEEMDAN, the original sequences are decomposed into multifrequency IMFs. Second, we use t‐test to determine the high‐frequency IMFs, low‐frequency IMFs, and trend sequence and reconstruct the high‐frequency IMFs into a new component sequence. Third, we use CNN‐LSTM model to predict these components separately and finally get the final prediction result by integration. This paper takes the USD/RMB exchange rate as research object, and the experimental results show that (1) the fluctuations of USD/RMB exchange rate are mainly affected by the trend sequence and low‐frequency IMFs and are less affected by high‐frequency IMFs. (2) The evaluation criterions RMSE, MAE, and MAPE of the ICEEMDAN‐CNN‐LSTM model are relatively small, with values of 0.0156, 0.0112, and 0.1679, respectively, indicating that the predictive performance of the model is optimal. (3) This paper has conducted various robust tests, all of which indicate that the proposed model has high prediction accuracy and stability. In summary, this paper has certain theoretical significance and application value.

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

Cited by 287 publications

References 47 publications

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Attribute Weighting Based K-Nearest Neighbor Using Gain Ratio

Stock Market Predictions Using Deep Learning

Forecasting USD/RMB exchange rate using the ICEEMDAN‐CNN‐LSTM model

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