Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background

By S. Imandoust, Mohammad Bolandraftar. 2013

534 citations recorded by the source corpus.

Subfields: Economics & Operations Research, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics & Operations Research

1. Petros Spachos
2. Hadis Karimipour
3. Syeda Manjia Tahsien

In Time-Series & Forecasting

1. Petros Spachos
2. Hadis Karimipour
3. Syeda Manjia Tahsien

Citations in the corpus

• Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “There lies the rationale of why KNN has also been adopted widely in contexts in which model interpretability is a requirement [186–189] .”
• Selecting appropriate machine learning methods for digital soil mapping (2020, unknown): “Major factors for selecting suitable ML algorithm(s) on DSM. Algorithm(s) Quantity of Hyperparameters Not Sensitive to Data Size Covariate Selection * Low Computation Time Interpretability MLR 0 ✕ ✕ ✓ ✓ KNN 1 ✓ ✕ ✓ ✕ SVR 2 ✓ ✕ ✓ ✕ Cubist 2 ✓ ✓ ✓ ✓ RF 3 ✓ ✓ ✕ ✕† ANN 8 ✕ ✕ ✕ ✕ ANN, Artificial Neural N”
• Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “Then, recalculation is performed based on the average value of node from every cluster; iii) Finally, the method redo its prior steps until it coincides to get the K-mean value [127]-[129].”