How to Deal with Noisy Data?

While working with artificial intelligence, machine learning, and reinforcement learning we may have come across different types of garbage values. These values sometimes may have affected the original signals. That is the reason, which may have disturbed the efficiency of the model, system, or component. There are different filters, techniques, and criteria available on the basis of which one can obtain better data or remove noisiness from the original data in order to get better results, accuracy, and precision. In this article, a discussion on noisy data and its remedy will be made. This article will provide a gateway to the newcomers in the field of machine learning, or deep learning who are facing such issues.

To go further one must know what basically noisy data is. Noisy data is basically meaningless data that doesn’t have any positive impact on the efficiency of the mode. The system and the model can’t understand this data. Machines often don’t interpret these data and hence end in giving different or unforeseen movements. Noisy data can be in the form of unstructured texts. While working with machine learning models, there is a point where the real-world data has been converted into the kind of data that can be understood by the machines. While dealing with such kind of data may have affected the data analysis which has to be done in machine learning. The affected tasks of machine learning involving noisy data are classification of data, association analysis of data, and clustering of data sets. The noise should have been detrimental so that data analysis cannot be affected and hence efficiency shouldn’t be disturbed.

In machine learning, artificial intelligence, and reinforcement learning there are different techniques with the help of which we can find the noisy data. Each of these techniques, along with its brief description is given as follows.

K fold method is a method with the help of which we can find noisy data and it is the most important technique which is used to find noisy data and is more commonly used. In this technique, cross-validation of each fold of the model has been observed and analyses are being done on the folds having smaller scores.
Manual Method. There is another method that is used conventionally in order to find and remove noisy data. This method is manually operated. In this method, the cross-validation score of each is predicted and recorded as well. After the filter, all the values have been obtained and analyzed simultaneously. In this way, the poor score record has been evaluated. This method is not only suitable for the detection of noisy data but also for the removal of noisy data as well. With the help of this technique, we can find why that noisy data occurred.
Another method in which we can detect noisy data is by assuming the normal data points inside a dataset. This method is known as anomaly detection based on density. This method highlights the normal data points based on their weights. In this way, the dense neighborhood or the neighbor having abnormalities can be detected and can be rectified at the same time as well.
There is another technique, which is somewhat similar to the previously discussed technique. In this technique, a cluster of data has been analyzed. Normal points have been added to the data and all the data that have fallen outside this normal point are regarded as noisy data. With the help of this technique, a cluster of data can be analyzed rather than analyzing each point which is time-consuming. The only drawback that we have faced in this technique is its accuracy.
Anomaly detection based on a support vector machine is a higher-ranked technique which is the upgraded version of the technique discussed earlier. In this technique, a soft boundary can be made, and validation techniques are started in order to find out the abnormalities in the data. The only difference between this technique and the previously discussed technique is the large data set that is required in this technique in order to find abnormalities. Among all the discussed techniques, this is one of the most accurate techniques which can be used by researchers and experts in order to remove data noises. (Enache & Sgarciu, 2015)
With the evolution of technology like machine learning and image processing, there are different fields that are taking over the world. There is another technique that is one the underrated techniques used to find the noises in the data and remove them. This technique is auto-encoder-based noisy data detection. Further research and experimentation have shown that this technique has outperformed the conventional methods or all the methods that have been discussed earlier when it comes to anomaly detection. This is one of the most important techniques used on the basis of probability in unsupervised machine learning.

Advantages of Removing Noisy Data from a Data Set in Machine Learning

Following are the different advantages that are linked with removing the noisy data in machine learning.

It can create problems and different meaningless values. With the help of these meaningless values, the machine or the model didn’t work up to its maximum efficiency hence, giving the wrong output.
The machine or the model starts generalizing from the noisy data which can be harmful to the model.
In unsupervised learning, the removal of noisy data is very important, so that proper data can be provided to the controller.