Granger Causality Driven AHP for Feature Weighted kNN

Bhattacharya, Gautam; Ghosh, Koushik; Chowdhury, Ananda S.

doi:10.1016/j.patcog.2017.01.018

Cited by 33 publications

(9 citation statements)

References 42 publications

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“…It is often used to solve nonlinear problems, such as credit ratings and bank customer rankings, in which the collected data do not always follow the theoretical linear assumption, thus it should be one of the first choices when there is little or no prior knowledge about the distribution data. In addition, it can successfully reduce the influences of the variables on the experimental processes [13]. It has higher forecasting accuracy and has no assumptions for the collected data, and particularly, it is not sensitive to the outliers.…”

Section: Introductionmentioning

confidence: 99%

“…Their proposed improved K-NN algorithm is applied to classification, regression, and missing data imputation with superior results. Bhattacharya et al [13] employs the weights obtained from the analytic hierarchy process (AHP) for different features to propose a weighted distance function for the K-NN algorithm. Their results demonstrate that the performance of the proposed K-NN classifier can receive improved results in terms of pairwise comparison of features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Fan¹,

Guo²,

Zheng³

et al. 2019

Energies

121

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In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Fan¹,

Guo²,

Zheng³

et al. 2019

Energies

121

View full text Add to dashboard Cite

show abstract

“…robustness [19][20][21][22][23][24][25][26][27][28][29][30][31] . In biological sciences, examples of such knowledge integration include inference of biological networks 24,32 and causal pathway modelling 33,34 .…”

Section: Integration Of Mechanistic Immunological Knowledge Into a Mamentioning

confidence: 99%

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

et al. 2020

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The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

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“…For supervised learning, the training samples include inputs and outputs (i.e., features and class labels), which results in a better result than unsupervised learning in most cases [ 12 ]. The supervised algorithm commonly used includes decision tree (DT) [ 13 ], naïve Bayes (NB) [ 14 ], k-nearest neighbor (kNN) [ 15 – 17 ], neural networks (NNs) [ 18 , 19 ], and support vector machine (SVM) [ 20 – 22 ]. Among them, SVM was first formally proposed by Cortes and Vapnik in 1995.…”

Section: Introductionmentioning

confidence: 99%

Improved barnacles mating optimizer algorithm for feature selection and support vector machine optimization

Jia

Sun

2021

Pattern Anal Applic

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With the rapid development of computer technology, data collection becomes easier, and data object presents more complex. Data analysis method based on machine learning is an important, active, and multi-disciplinarily research field. Support vector machine (SVM) is one of the most powerful and fast classification models. The main challenges SVM faces are the selection of feature subset and the setting of kernel parameters. To improve the performance of SVM, a metaheuristic algorithm is used to optimize them simultaneously. This paper first proposes a novel classification model called IBMO-SVM, which hybridizes an improved barnacle mating optimizer (IBMO) with SVM. Three strategies, including Gaussian mutation, logistic model, and refraction-learning, are used to improve the performance of BMO from different perspectives. Through 23 classical benchmark functions, the impact of control parameters and the effectiveness of introduced strategies are analyzed. The convergence accuracy and stability are the main gains, and exploration and exploitation phases are more properly balanced. We apply IBMO-SVM to 20 real-world datasets, including 4 extremely high-dimensional datasets. Experimental results are compared with 6 state-of-the-art methods in the literature. The final statistical results show that the proposed IBMO-SVM achieves a better performance than the standard BMO-SVM and other compared methods, especially on high-dimensional datasets. In addition, the proposed model also shows significant superiority compared with 4 other classifiers.

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Granger Causality Driven AHP for Feature Weighted kNN

Cited by 33 publications

References 42 publications

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Improved barnacles mating optimizer algorithm for feature selection and support vector machine optimization

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