Weighted feature-based classification of time series data
P Ravikumar, VS Devi - 2014 IEEE Symposium on …, 2014 - ieeexplore.ieee.org
2014 IEEE Symposium on Computational Intelligence and Data Mining …, 2014•ieeexplore.ieee.org
Classification is one of the most popular techniques in the data mining area. In supervised
learning, a new pattern is assigned a class label based on a training set whose class labels
are already known. This paper proposes a novel classification algorithm for time series data.
In our algorithm, we use four parameters and based on their significance on different
benchmark datasets, we have assigned the weights using simulated annealing process. We
have taken the combination of these parameters as a performance metric to find the …
learning, a new pattern is assigned a class label based on a training set whose class labels
are already known. This paper proposes a novel classification algorithm for time series data.
In our algorithm, we use four parameters and based on their significance on different
benchmark datasets, we have assigned the weights using simulated annealing process. We
have taken the combination of these parameters as a performance metric to find the …
Classification is one of the most popular techniques in the data mining area. In supervised learning, a new pattern is assigned a class label based on a training set whose class labels are already known. This paper proposes a novel classification algorithm for time series data. In our algorithm, we use four parameters and based on their significance on different benchmark datasets, we have assigned the weights using simulated annealing process. We have taken the combination of these parameters as a performance metric to find the accuracy and time complexity. We have experimented with 6 benchmark datasets and results shows that our novel algorithm is computationally fast and accurate in several cases when compared with 1NN classifier.
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