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A robust spectral clustering algorithm for sub-Gaussian mixture models with outliers

Srivastava, Prateek R.; Sarkar, Purnamrita; … - In: Operations research 71 (2023) 1, pp. 224-244

Persistent link: https://www.econbiz.de/10014308484

Ensembled LSTM with walk forward optimization in algorithmic trading

Chojnacki, Karol; Ślepaczuk, Robert - 2023

Persistent link: https://www.econbiz.de/10014308890

A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

Bansal, Malti; Goyal, Apoorva; Choudhary, Apoorva - In: Decision analytics journal 3 (2022), pp. 1-21

machine learning algorithms namely-K-Nearest Neighbor (KNN), Genetic Algorithm (GA), Support Vector Machine (SVM), Decision … exhaustively reviewed and researched in all aspects qualitatively and quantitatively, wherein the LSTM network and SVM algorithm …

Persistent link: https://www.econbiz.de/10013339256

Boosting ant colony optimization via solution prediction and machine learning

Sun, Yuan; Wang, Sheng; Shen, Yunzhuang; Li, Xiaodong; … - In: Computers & operations research : and their … 143 (2022), pp. 1-16

Persistent link: https://www.econbiz.de/10013343259

Prediction of IPL Match Performance Based on Batsman Category Using Machine Learning Algorithm

Singh, Chandrani; Jadhav, Ramesh; Dr. Bharti P … - 2022

In the world of business, firms have started adopting technologies to develop and grow their businesses. To sustain in this competitive market there is a dire need for better performance and precision in prediction .To achieve this goal various businesses, require to accurately analyze and...

Persistent link: https://www.econbiz.de/10013293745

Supervised learning for arrival time estimations in restaurant meal delivery

Hildebrandt, Florentin D.; Ulmer, Marlin Wolf - In: Transportation science 56 (2022) 4, pp. 1058-1084

Persistent link: https://www.econbiz.de/10013365649

Beta Forecasting with Realized Beta Estimators and Machine Learning Algorithms

Doan, Bao Huy; Jayasuriya, Dulani; Lee, John B.; … - 2021

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...

Persistent link: https://www.econbiz.de/10013251197

A comparative study of statistical machine learning methods for condition monitoring of electric drive trains in supply chains

Lahmiri, Salim - In: Supply chain analytics 2 (2023), pp. 1-7

-nearest neighbor algorithm, naïve Bayes, kernel naïve Bayes, decision trees, and support vector machine to distinguish between eleven … analysis (84.5%), k-NN algorithm (80.5%), and Gaussian naïve Bayes (71.3%). Accordingly, the choice of statistical machine … learning algorithm influences classification accuracy related to electric drive fault diagnosis. In addition, decision trees …

Persistent link: https://www.econbiz.de/10014516481

A new approach to capacity scaling augmented with unreliable machine learning predictions

Rutten, Daan; Mukherjee, Debankur - In: Mathematics of operations research 49 (2024) 1, pp. 476-508

Persistent link: https://www.econbiz.de/10014527953

Bankruptcy prediction and stress quantification using support vector machine : evidence from Indian banks

Shrivastava, Santosh Kumar; Ramudu, P. Janaki - In: Risks : open access journal 8 (2020) 2/52, pp. 1-22

using a two-step feature selection technique. First, a relief algorithm is used for primary screening of useful features …

Persistent link: https://www.econbiz.de/10012292870