<p>First of all, Deep Learning isn't a mythical hammer you can throw at every problem and expect better results. It requires careful analysis of your problem, choosing the right method, crafting your network, properly setting up your training, and <em>only then, <a href="https://arxiv.org/abs/1803.03635" rel="nofollow noreferrer">with a lot of luck</a></em> will you see significantly better results than classical methods.</p>

<p>From what you describe (and without any more details about your implementation), it seems to me that there could have been several things going wrong:</p>

<ol>
<li>Your task is simply not designed for a neural network. Some tasks are still better solved with classical methods, since they <em>manually</em> account for patterns in your data, or distill your advanced reasoning/knowledge into a prediction. You might not be directly aware of it, but sometimes neural networks are just overkill.</li>
<li>You don't describe how your 11000 instances are distributed with respect to the target classes, how big the input is, what kind of preprocessing you are performing for either method, etc, etc. Maybe your data is simply processed wrong, your training is diverging due to unfortunate parameter setups, or plenty of other things.</li>
</ol>

<p>To expect a reasonable answer, you would have to share at least a bit of code regarding the implementation of your task, and parameters you are using for training.</p>
Dataset with only values (0,1,-1) with LSTM or CNN is giving 50% accuracy where as RF, SVM, ELM, Neural networks are giving above 90%
<p>I have a dataset with 11k instances containing 0s,1s and -1s. I heard that deep learning can be applied to feature values.Hence applied the same for my dataset but surprisingly it resulted in less accuracy (&lt;50%) compared to traditional machine learning algos (RF,SVM,ELM). Is it appropriate to apply deep learning algos to feature values for classification task? Any suggestion is greatly appreciated.</p>