Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 304
Author(s):  
Hayder Naser Khraibet Al-Behadili ◽  
Ku Ruhana Ku-Mahamud ◽  
Rafid Sagban
Keyword(s):  
Classification Algorithm ◽  
Pruning Technique

A DISCRIMINATIVE SHAPELETS TRANSFORMATION FOR TIME SERIES CLASSIFICATION

2014 ◽  
Vol 28 (06) ◽  
pp. 1450014 ◽  
Author(s):  
JI-DONG YUAN ◽  
ZHI-HAI WANG ◽  
MENG HAN
Keyword(s):  
Time Series ◽  
Great Influence ◽  
Classification Algorithm ◽  
Experimental Results ◽  
Time Series Classification ◽  
Classification Methods ◽  
Classification Result ◽  
Benchmark Datasets ◽  
Second Category ◽  
Pruning Technique

Time series shapelets are subsequences of time series that could be representative of a class. Shapelets-based time series classification methods can be divided into two large categories. The first category integrates shapelets selection within the process of constructing classifier; while the second category disconnects the process of finding shapelets from the classification algorithm by adopting a shapelet transformation. However, there are two important limitations of shapelet transformation. First, the number of shapelets selected for transformation has great influence on classification result, but it is difficult to decide the quantity of shapelets which yields the best data for classification. Second, similar shapelets always exist among the selected shapelets in previous algorithms. In our work, the latter problem is addressed by introducing an efficient and effective pruning technique, it filters similar shapelets and decreases the number of candidate shapelets at the same time. Then, we propose a novel shapelet coverage method to select shapelets for a given dataset. The final selected shapelets are named after Discriminative Shapelets. Our experimental results demonstrate that, on the classic benchmark datasets used for time series classification, shapelet pruning and coverage method outperforms ShapeletFilter.

A Multi-Label Classification Algorithm Based on Random Walk Model

2010 ◽  
Vol 33 (8) ◽  
pp. 1418-1426 ◽  
Author(s):  
Wei ZHENG ◽  
Chao-Kun WANG ◽  
Zhang LIU ◽  
Jian-Min WANG
Keyword(s):  
Random Walk ◽  
Classification Algorithm ◽  
Random Walk Model

Uncertain data decision tree classification algorithm

2009 ◽  
Vol 29 (11) ◽  
pp. 3092-3095 ◽  
Author(s):  
Fang LI ◽  
Yi-yuan LI ◽  
Chong WANG
Keyword(s):  
Decision Tree ◽  
Uncertain Data ◽  
Classification Algorithm ◽  
Decision Tree Classification

Multi-view semi-supervised collaboration classification algorithm with combination of agreement and disagreement label rules

2013 ◽  
Vol 33 (11) ◽  
pp. 3090-3093
Author(s):  
Chongchong YU ◽  
Yu LIU ◽  
Li TAN ◽  
Lili SHANG ◽  
Meng MA
Keyword(s):  
Classification Algorithm
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