Semi-Supervised Machine Learning Algorithms

Artificial intelligence is a system that can not only solve assigned tasks but also learn how to solve new problems, including creative ones. Previously, this process was available only to the human brain, but now artificially created programs can also do this. The AI system needs learning algorithms to study and create corresponding patterns that can improve the program and provide better results in the future.

Machine learning algorithms can be divided into controlled (it includes naive Bayesian classification, decision tree, least squares method, logistic regression, support vector method, ensemble method), uncontrolled (clustering algorithms, principal component method, singular decomposition, analysis of independent components), and semi-supervised learning (mixed type), online learning, reinforcement learning, transductive learning, meta-learning, active learning. 

Controlled methods presuppose that the previously used array can be useful for the ongoing  task also. If the current task was not performed earlier, it is possible for machines to learn how to make it using learning algorithms  So in the controlled methods previously used array can be useful for the ongoing task also. Such methods are used in face recognition systems, spam selection in email, calculating the probability of a natural disaster, predicting  the success of advertising and so on. Uncontrolled methods are applied if the dataset is not labeled, and it is needed to find implicit relations in it. The examples of using uncontrolled systems include: 

• observing the interaction of a large number of genes in biology;
• in sociological research, speech and picture recognition;
• the diagnosis of complex computing systems and mechanisms.

Semi-supervised machine learning algorithms represent a hybrid of the supervised and unsupervised systems. The controlled algorithm has the data entered by the developer with all the variables, and all these elements together provide a solution to a given issue. If the developer cannot specify all the exact data or can specify it only partially, then this method of learning algorithms is perfect for solving problems in such conditions.

The process starts when the developer enters the data and marks it. Then the program will be able to predict the solution for untagged data based on the patterns identified in the system . It will be processed using the previous experience of solving such tasks by the program. 

Higher accuracy of ML solutions can be gained on condition that among the many untagged data, at least a few labeled ones are placed. Then the machine will be able to draw logical conclusions based on given data. Nevertheless, an observer is required to control this process and decrypt data for future usage.

If you want to deep dive into the topic of ML algorithms, you should pay attention to the following theories that provide a better understanding of how all the AI systems work:

• continuity assumption (points that exist very close with a high probability will have the same output label)
• cluster assumption (if to divide all data into different clusters, all the data of one such group is highly probable to have the same output label)
• manifold assumption (the size of the input space is much larger than the area on which the data is placed)

Semi-supervised algorithms are applied in a variety of industries starting from fintech and ending up with entertaining apps. In banking, ML systems play a vital role since they help organizations to build data security. There is a sample of people who are currently customers of the bank. The developer must create a program that makes it easier for the administration to detect fraud. The developer knows only a few cases of cybercrime, and he enters all of them into the database.

He does not know about the remaining cases, and his task is to detect all instances to prevent fraud in the future. Since the developer does not mark the data that needs to be identified, it  should be found by the machine to continue working. The observer marks the known data for the program and enables the system to learn using this information. In this case, the system will be trained on the basis of existing patterns and algorithms introduced by the developer, but will also detect data sets that do not have an exact result and work with them.

In such conditions, semi-supervised algorithms work the best as they combine the features of both controlled and uncontrolled systems. Human intervention is needed to translate or decrypt the collected data, conduct experiments that require third-party objects or locations and physical presence.

However, big data systems require a different approach. It might be challenging for the developer to label patterns manually, so he needs an automated system to substitute human work.  In this case, specially trained professionals are needed to work with databases. Although human-based approach is reasonable in this case, it might be resource-consuming and inefficient. 

Sumi-supervised systems have been widely applied in education for the last several years. For example, when a teacher at  school gives tasks and solves them together with students, students enter particular data to find the right solution to it.

This approach is similar to the one used with marked data in a program. Then the teacher gives homework, and students learn to solve tasks on their own using familiar algorithms.

However, only a share of tasks have the same structure and solution algorithm. Therefore, under the supervision of a teacher, students gradually learn to solve all new examples of problems, which were not initially disassembled in the classroom. This approach to education is highly effective. Thus, it has been successfully applied in AI and ML systems. 

To determine which machine learning algorithm is best for your project or product, you have to evaluate the urgency of the task, think of the necessary solution, estimate time and resources required to address the challenge and determine the amount, size and type of input data. This deep evaluation will provide you with an understanding of which algorithm is needed and lead you to the most optimal solution.