Читать книгу Data Analytics in Bioinformatics - Группа авторов - Страница 17

This is a very common learning mechanism in ML and used by most of the newcomer researchers in their respective fields. This learning mechanism trains the machine by using a labeled dataset in the form of compressed input–output pair as depicted in Refs. [13–15]. These datasets are available in continuous or discrete form. But the important thing is, it needs supervision with an appropriate training model. As supervised learning predicts accurate results [16], hence it is mostly used for Regression analysis and classification purposes. Figure 1.4 shows the execution model of supervised learning.

The figure shows that in supervised learning, a given set of input attributes (i. e. A₁, A₂, A₃, A₄ … … A_k) along with their output attributes (i.e. B₁, B₂, B₃, B₄ … … … B_k) are kept in a knowledge dataset. The Learning Algorithm takes an input A_i and executes with its model and produces the result B_i as the desired output. Supervised Learning has its importance in the field of Bioinformatics as concerning the heart disease scenario where inputs can be a lot of symptoms of heart diseases such as High Cholesterol, Chest Pain, and Blood Pressure, etc. and the output could be a person suffering from heart disease or not. Now all these inputs are passed on to the learning algorithm where it gets trained and if a new input is passed through the model then the machine gives an expected output. If the expected output’s accuracy is not up to the mark then there is a need for modification or up-gradation in the model.

Figure 1.4 Block diagram of supervised learning.

An example of supervised learning could be of a person who felt that he has a high cholesterol level and a chest pain and went to the doctor for a check-up. The Doctor fed the inputs given by the patient to the machine. The Machine predicted and told the doctor that the patient is suffering from a cardiac issue in his heart. It acts as an analogy to the supervised learning as the inputs given by the patient are the independent variables and their corresponding output from the machine acts as the dependent attribute. The Machine acted as a model that predicted and gave a relevant output as it is trained by similar inputs. Supervised Learning is itself a huge subfield of ML and useful for a variety of techniques used in research work. These techniques include Regression Analysis, Artificial Neural Networks (ANN), Support Vector Machines (SVM), etc.