Dynamic feature weighting in nearest neighbor classifiers

2005 ◽  
Author(s):  
Xin Tong ◽  
P. Ozturk ◽  
Mingyang Gu
Keyword(s):  
Nearest Neighbor ◽  
Feature Weighting ◽  
Dynamic Feature ◽  
Nearest Neighbor Classifiers

Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers

2014 ◽  
Vol 47 (12) ◽  
pp. 3941-3948 ◽  
Author(s):  
José A. Sáez ◽  
Joaquín Derrac ◽  
Julián Luengo ◽  
Francisco Herrera
Keyword(s):  
Nearest Neighbor ◽  
Feature Weighting ◽  
Weighting Schemes ◽  
Statistical Computation ◽  
Nearest Neighbor Classifiers ◽  
Data Estimation

scholarly journals Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 246
Author(s):  
Vagan Terziyan ◽  
Anton Nikulin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Nearest Neighbor ◽  
Geographical Information ◽  
Two Dimensional ◽  
Prototype Selection ◽  
Data Space ◽  
Important Concern ◽  
Traditional Classification ◽  
Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

Large-margin nearest neighbor classifiers via sample weight learning

2011 ◽  
Vol 74 (4) ◽  
pp. 656-660 ◽  
Author(s):  
Qinghua Hu ◽  
Pengfei Zhu ◽  
Yongbin Yang ◽  
Daren Yu
Keyword(s):  
Nearest Neighbor ◽  
Sample Weight ◽  
Large Margin ◽  
Weight Learning ◽  
Nearest Neighbor Classifiers

scholarly journals Stronger Automation for Flyspeck by Feature Weighting and Strategy Evolution

10.29007/5gzr ◽  
2018 ◽  
Author(s):  
Cezary Kaliszyk ◽  
Josef Urban
Keyword(s):  
Nearest Neighbor ◽  
Feature Weighting ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Hol Light ◽  
Distance Weighted ◽  
Neighbor Classifier

Two complementary AI methods are used to improve the strength of the AI/ATP service for proving conjectures over the HOL Light and Flyspeck corpora. First, several schemes for frequency-based feature weighting are explored in combination with distance-weighted k-nearest-neighbor classifier. This results in 16% improvement (39.0% to 45.5% Flyspeck problems solved) of the overall strength of the service when using 14 CPUs and 30 seconds. The best premise-selection/ATP combination is improved from 24.2% to 31.4%, i.e. by 30%. A smaller improvement is obtained by evolving targetted E prover strategies on two particular premise selections, using the Blind Strategymaker (BliStr) system. This raises the performance of the best AI/ATP method from 31.4% to 34.9%, i.e. by 11%, and raises the current 14-CPU power of the service to 46.9%.

On visualization and aggregation of nearest neighbor classifiers

2005 ◽  
Vol 27 (10) ◽  
pp. 1592-1602 ◽  
Author(s):  
A.K. Ghosh ◽  
P. Chaudhuri ◽  
C.A. Murthy
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Classifiers
Sign in / Sign up

Export Citation Format

Share Document