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K Nearest Neighbors

K Nearest Neighbors. Saed Sayad. KNN - Definition. KNN is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure. KNN – different names. K-Nearest Neighbors Memory-Based Reasoning Example-Based Reasoning Instance-Based Learning

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K Nearest Neighbors

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  1. K Nearest Neighbors Saed Sayad www.ismartsoft.com

  2. KNN - Definition KNN is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure www.ismartsoft.com

  3. KNN – different names • K-Nearest Neighbors • Memory-Based Reasoning • Example-Based Reasoning • Instance-Based Learning • Case-Based Reasoning • Lazy Learning www.ismartsoft.com

  4. KNN – Short History • Nearest Neighbors have been used in statistical estimation and pattern recognition already in the beginning of 1970’s (non-parametric techniques). • Dynamic Memory: A theory of Reminding and Learning in Computer and People (Schank, 1982). • People reason by remembering and learn by doing. • Thinking is reminding, making analogies. • Examples = Concepts??? www.ismartsoft.com

  5. KNN Classification Loan$ Age www.ismartsoft.com

  6. KNN Classification – Distance Euclidean Distance www.ismartsoft.com

  7. KNN Classification – Standardized Distance Standardized Variable www.ismartsoft.com

  8. KNN Regression - Distance www.ismartsoft.com

  9. KNN Regression – Standardized Distance www.ismartsoft.com

  10. KNN – Number of Neighbors • If K=1, select the nearest neighbor • If K>1, • For classification select the most frequent neighbor. • For regression calculate the average of K neighbors. www.ismartsoft.com

  11. Distance – Categorical Variables www.ismartsoft.com

  12. Instance Based Reasoning • IB1 is based on the standard KNN • IB2 is incremental KNN learner that only incorporates misclassified instances into the classifier. • IB3 discards instances that do not perform well by keeping success records. www.ismartsoft.com

  13. Case Based Reasoning www.ismartsoft.com

  14. KNN - Applications • Classification and Interpretation • legal, medical, news, banking • Problem-solving • planning, pronunciation • Function learning • dynamic control • Teaching and aiding • help desk, user training www.ismartsoft.com

  15. Summary • KNN is conceptually simple, yet able to solve complex problems • Can work with relatively little information • Learning is simple (no learning at all!) • Memory and CPU cost • Feature selection problem • Sensitive to representation www.ismartsoft.com

  16. Questions? www.ismartsoft.com

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