Pattern Synthesis Using Fuzzy Partitions of the Feature Set for Nearest Neighbor Classifier Design

Lecture Notes in Computer Science - Multi-disciplinary Trends in Artificial Intelligence ◽

10.1007/978-3-642-25725-4_11 ◽

2011 ◽

pp. 124-135

Author(s):

Pulabaigari Viswanath ◽

S. Chennakesalu ◽

R. Rajkumar ◽

M. Raja Sekhar

Keyword(s):

Nearest Neighbor ◽

Pattern Synthesis ◽

Classifier Design ◽

Nearest Neighbor Classifier ◽

Fuzzy Partitions ◽

Neighbor Classifier

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A pattern synthesis technique with an efficient nearest neighbor classifier for binary pattern recognition

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. ◽

10.1109/icpr.2004.1333791 ◽

2004 ◽

Author(s):

R. Viswanath ◽

M. Narasimha Murty ◽

S. Bhatnagar

Keyword(s):

Pattern Recognition ◽

Nearest Neighbor ◽

Pattern Synthesis ◽

Nearest Neighbor Classifier ◽

Synthesis Technique ◽

Neighbor Classifier

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A bootstrap technique for nearest neighbor classifier design

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/34.566814 ◽

1997 ◽

Vol 19 (1) ◽

pp. 73-79 ◽

Author(s):

Y. Hamamoto ◽

S. Uchimura ◽

S. Tomita

Keyword(s):

Nearest Neighbor ◽

Classifier Design ◽

Nearest Neighbor Classifier ◽

Bootstrap Technique ◽

Neighbor Classifier

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A vector quantization method for nearest neighbor classifier design

Pattern Recognition Letters ◽

10.1016/j.patrec.2004.01.012 ◽

2004 ◽

Vol 25 (6) ◽

pp. 725-731 ◽

Author(s):

Chen-Wen Yen ◽

Chieh-Neng Young ◽

Mark L Nagurka

Keyword(s):

Vector Quantization ◽

Nearest Neighbor ◽

Quantization Method ◽

Classifier Design ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

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Overlap pattern synthesis with an efficient nearest neighbor classifier

Pattern Recognition ◽

10.1016/j.patcog.2004.10.007 ◽

2005 ◽

Vol 38 (8) ◽

pp. 1187-1195 ◽

Author(s):

P. Viswanath ◽

Narasimha Murty ◽

Shalabh Bhatnagar

Keyword(s):

Nearest Neighbor ◽

Pattern Synthesis ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

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A High-Voltage Electric Switch Classification System Based on K-Nearest Neighbor Classifier

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344925 ◽

2020 ◽

Author(s):

Haien Wang ◽

Jing Zhang ◽

Yang Zhao ◽

Jun Wang ◽

Xiaorong Du

Keyword(s):

High Voltage ◽

Classification System ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

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Quad division prototype selection-based k-nearest neighbor classifier for click fraud detection from highly skewed user click dataset

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2021.05.015 ◽

2021 ◽

Author(s):

Deepti Sisodia ◽

Dilip Singh Sisodia

Keyword(s):

Nearest Neighbor ◽

Fraud Detection ◽

Prototype Selection ◽

K Nearest Neighbor ◽

Click Fraud ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

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Nearest neighbor classifier based on riemannian metric in radar target recognition

IEEE International Radar Conference, 2005. ◽

10.1109/radar.2005.1435946 ◽

2005 ◽

Author(s):

Meng Jicheng ◽

Yang Wanlin

Keyword(s):

Nearest Neighbor ◽

Target Recognition ◽

Riemannian Metric ◽

Radar Target ◽

Nearest Neighbor Classifier ◽

Radar Target Recognition ◽

Neighbor Classifier

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Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes

Lecture Notes in Computer Science - Hybrid Artificial Intelligence Systems ◽

10.1007/978-3-319-07617-1_52 ◽

2014 ◽

pp. 597-606 ◽

Author(s):

José A. Sáez ◽

Joaquín Derrac ◽

Julián Luengo ◽

Francisco Herrera

Keyword(s):

Nearest Neighbor ◽

Feature Weighting ◽

Weighting Schemes ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

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k-Nearest Neighbor Classifier and Supervised Clustering

Data Mining ◽

10.1201/b15288-7 ◽

2013 ◽

pp. 117-137

Author(s):

Nong Ye

Keyword(s):

Nearest Neighbor ◽

K Nearest Neighbor ◽

Supervised Clustering ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

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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%.

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