How to improve classification accuracy for machine learning

Question

I have used the extreme learning machine for classification purpose and found that my classification accuracy is only at 70+% which leads me to use the ensemble method by creating more classification model and testing data will be classified based on the majority of the models' classification. However, this method only increase classification accuracy by a small margin. Can I asked what are the other methods which can be used to improve classification accuracy of the 2 dimension linearly inseparable dataset ?

Solution 1

Your question is very broad ... There's no way to help you properly without knowing the real problem you are treating. But, some methods to enhance a classification accuracy, talking generally, are:

1 - Cross Validation : Separe your train dataset in groups, always separe a group for prediction and change the groups in each execution. Then you will know what data is better to train a more accurate model.

2 - Cross Dataset : The same as cross validation, but using different datasets.

3 - Tuning your model : Its basically change the parameters you're using to train your classification model (IDK which classification algorithm you're using so its hard to help more).

4 - Improve, or use (if you're not using) the normalization process : Discover which techniques (change the geometry, colors etc) will provide a more concise data to you to use on the training.

5 - Understand more the problem you're treating... Try to implement other methods to solve the same problem. Always there's at least more than one way to solve the same problem. You maybe not using the best approach.

Solution 2

Enhancing a model performance can be challenging at times. I’m sure, a lot of you would agree with me if you’ve found yourself stuck in a similar situation. You try all the strategies and algorithms that you’ve learnt. Yet, you fail at improving the accuracy of your model. You feel helpless and stuck. And, this is where 90% of the data scientists give up. Let’s dig deeper now. Now we’ll check out the proven way to improve the accuracy of a model:

1. Add more data
2. Treat missing and Outlier values
3. Feature Engineering
4. Feature Selection
5. Multiple algorithms
6. Algorithm Tuning
7. Ensemble methods
8. Cross Validation

if you feel the information is lacking then this link should you learn, hopefully can help : https://www.analyticsvidhya.com/blog/2015/12/improve-machine-learning-results/ sorry if the information I give is less satisfactory

Comments

[TK Alive]

Hi, thanks for your reply. But I did mention what algorithm i used, the extreme learning machine. I believed cross validation is the k fold cross validation to determine the number of neurons i used is the optimal one which i did in another way. In addition, I have already normalized the data if that helps. Also as mentioned in my question i would like to know other methods which can be used to improve classification accuracy of the 2 dimension linearly inseparable dataset (only 2 parameters to classify instance into category)