Open AccessAN OPTIMIZED ADABOOST ALGORITHM BASED ON K-MEANS CLUSTERING
Author(s) -
Peng Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1856/1/012021
Subject(s) - adaboost , cluster analysis , computer science , statistical classification , algorithm , artificial intelligence , pattern recognition (psychology) , euclidean distance , data mining , machine learning , support vector machine
Classification plays an important role on data mining techniques. AdaBoost is a classic upgrade algorithm on data classification. The Optimization algorithm on AdaBoost emerged is endless. In this paper we make some improvement on the base of a nonlinear AdaBoost algorithm based on statistics for K-nearest neighbors. In the basic algorithm, the prediction accuracies were improved more or less while it took plenty of time to calculate the Euclidean distance between the instance and all the samples. So we raise an improved method to shorten the computing time with K-means Clustering algorithm, and the classification accuracies will be as accurate as the original. Experiment results show that improvement is an effective method while the number of samples is large. And the bigger the number of the samples is, the more time can be saving in the stage of classification for the instance.