Methods of Data Labeling in Machine Learning
Accruing a large amount of data is relatively simple. Data can be scraped, created or copied and then be stored in huge data storages.
A key driver in developing an intelligent model, however, is not just a sheer mass of data but also an effective strategy to intelligently label data to add structure and sense to the data. Data labeling can, therefore, be described as a way to organize information depending on its content.
This content determines the tag or label to be assigned to a specific piece of information after it has been processed. With such importance to be attached to data labeling, what are the most effective strategies?
Labeling can be done manually by a human or automatically by a machine. Manual labeling can either be done in-house, get crowdsourced or outsourced to individuals or companies. Considering the sheer amount of data however, manually labeling data is ineffective, costs time and money.
In order to allow automatic labeling, the model has to be enabled to understand what is depicted in the picture or written in the text when information is being processed. It has to be trained to know which tag should be attached to each data unit. To make this possible, a person needs to teach a machine to recognize the patterns automatically by running learning algorithms for labeled datasets. This is designed to simulate the human decision-making process.
Researches use 3 types of learning algorithms to allow AI systems to analyze and learn from input data independently. Reinforcement Learning, Supervised Learning and Unsupervised Learning.
Reinforcement Learning
The method utilizes the trial-and-error approach to make predictions within a specific context using feedback from their own experience. Over time the model will get better at making predictions and resolves classification and regression problems.
Supervised Learning
This method requires a huge amount of manually labeled data. Through this method, the model simply compares already labeled data with newly received data to find errors and inconsistencies. The model is then modified accordingly. It learns how to predict the probability of future events occurring and is mostly used to anticipate fraudulent credit card transactions or analyze historical data. During a very time-consuming process, a labeling mistake or inaccuracies of the input can also lead to wrong predictions and a wrong output.
Unsupervised Learning
The method leverages raw or unstructured data. It is used for more complex processes because its goal is to find the structure on its own and organize the data into a group of clusters. This type of learning is good for transactional data like identifying customer segments with the same attributes to treat them similarly in marketing campaigns.
Challenges
The main issues with data processing, labeling, classification, and analysis are related to optimization of data presentation and storage, construction of fast information retrieval algorithms, and design of recommender systems.
Moreover, because each company or individual uses and analyzes its data differently it should invest its unique mechanisms to label data for its deep learning models.
