@inproceedings{
 title = {Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: Empirical study},
 type = {inproceedings},
 year = {2018},
 keywords = {Knn,Prototype selection,Sensor data},
 volume = {2017-Janua},
 id = {b79c8c63-1336-3780-8bfc-49515955b9a8},
 created = {2018-11-21T20:30:20.554Z},
 file_attached = {false},
 profile_id = {97193e8c-2867-3b74-b62f-1c69932d41b7},
 last_modified = {2018-11-21T20:51:31.299Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2017 IEEE. This work presents a comparative study of prototype selection (PS) algorithms. Such a study is done over data-from-sensor acquired by an embedded system. Particularly, five flexometers are used as sensors, which are located inside a glove aimed to read sign language. Measures were taken to quantify the balance between classification performance and reduction training set data (QCR) with k neighbors equal to 3 and 1 to force the classifier (kNN) to the maximum. Two tests were used: (a)the QCR performance and (b) the embedded system decision in real proves. As result the Random Mutation Hill Climbing (RMHC) algorithm is considered the best option to choose in this data type with removed instances at 87% and classification performance at 82% in software tests, also the classifier kNN must be with k=3 to improve the classification performance. In a real situation, with the algorithm implemented. The system makes correct decisions at 81% with 5 persons doing sign language in real time.},
 bibtype = {inproceedings},
 author = {Rosero-Montalvo, P. and Peluffo-Ordonez, D.H. and Umaquinga, A. and Anaya, A. and Serrano, J. and Rosero, E. and Vasquez, C. and Suarez, L.},
 doi = {10.1109/ETCM.2017.8247530},
 booktitle = {2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017}
}

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This work presents a comparative study of prototype selection (PS) algorithms. Such a study is done over data-from-sensor acquired by an embedded system. Particularly, five flexometers are used as sensors, which are located inside a glove aimed to read sign language. Measures were taken to quantify the balance between classification performance and reduction training set data (QCR) with k neighbors equal to 3 and 1 to force the classifier (kNN) to the maximum. Two tests were used: (a)the QCR performance and (b) the embedded system decision in real proves. As result the Random Mutation Hill Climbing (RMHC) algorithm is considered the best option to choose in this data type with removed instances at 87% and classification performance at 82% in software tests, also the classifier kNN must be with k=3 to improve the classification performance. In a real situation, with the algorithm implemented. The system makes correct decisions at 81% with 5 persons doing sign language in real time.","bibtype":"inproceedings","author":"Rosero-Montalvo, P. and Peluffo-Ordonez, D.H. and Umaquinga, A. and Anaya, A. and Serrano, J. and Rosero, E. and Vasquez, C. and Suarez, L.","doi":"10.1109/ETCM.2017.8247530","booktitle":"2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017","bibtex":"@inproceedings{\n title = {Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: Empirical study},\n type = {inproceedings},\n year = {2018},\n keywords = {Knn,Prototype selection,Sensor data},\n volume = {2017-Janua},\n id = {b79c8c63-1336-3780-8bfc-49515955b9a8},\n created = {2018-11-21T20:30:20.554Z},\n file_attached = {false},\n profile_id = {97193e8c-2867-3b74-b62f-1c69932d41b7},\n last_modified = {2018-11-21T20:51:31.299Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2017 IEEE. This work presents a comparative study of prototype selection (PS) algorithms. Such a study is done over data-from-sensor acquired by an embedded system. Particularly, five flexometers are used as sensors, which are located inside a glove aimed to read sign language. Measures were taken to quantify the balance between classification performance and reduction training set data (QCR) with k neighbors equal to 3 and 1 to force the classifier (kNN) to the maximum. Two tests were used: (a)the QCR performance and (b) the embedded system decision in real proves. As result the Random Mutation Hill Climbing (RMHC) algorithm is considered the best option to choose in this data type with removed instances at 87% and classification performance at 82% in software tests, also the classifier kNN must be with k=3 to improve the classification performance. In a real situation, with the algorithm implemented. 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