I am performing time series classification using accelerometer data. I want to use a deep learning method for my project. I am classifying accelerometer data into various road conditions. I have a labeled dataset for various road conditions. I am presently using LSTM to perform the time series classification
I have a working LSTM classification code which runs perfectly on a different dataset.
# The code directory name is
--Description of the working project
--Dataset used UCI HAR dataset
--Result 70 percent Accuracy
--- This was just to justify that the code works to check please
I am trying a different dataset with the same structure and format. The dataset and project is inside the rar file name [login to view URL]
I am getting strange results like 31% accuracy and a confusion matrix in which only a single class is showing any [login to view URL] attachment name of the confusion matrix is [login to view URL]
The training and testing dataset is balanced among various classes.
When I use the same dataset and ensemble machine learning method, I obtain 50% percent accuracy using an ensemble machine learning method.
When I use the same dataset and calculate features and then use ensemble machine learning method, I obtain 78% percent accuracy with a better confusion matrix. Confusion matrix of Ensemble machine learning classifier is present in the attachment name [login to view URL]
I want to understand what I am doing wrong to get strange results with LSTM.
Things to try:
1. Trying hyperparameter optimization changing the parameters such as learning rate, number of hidden layers etc.
2. Come up with a feature to train deep learning neural network.
The rar file having the Code having the problem is [login to view URL]
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Lead Data Scientist with more than 100 top end machine learning projects under my belt. I have worked in one of Asia's top financial firm as a Data Scientist. One of my Proposals also got accepted for India's top univ Daha Fazla