Liver Disease Detection Using Grey Wolf Optimization and Random Forest Classification

Machine Learning

Over the past two decades machine learning has become one of the mainstays of information technology and, with that, a rather central, albeit usually hidden, part of people’s life. With the ever increasing amounts of data becoming available, there is good reason to believe that smart data analysis will become even more pervasive as a necessary ingredient for technological progress.

Machine learning can appear in many guises. Presently talk about various applications, the sorts of information manage, lastly, formalize the issues in a to some degree progressively adapted style. The latter is key to avoid reinventing the wheel for every new application. Instead, much of the art of machine learning is to reduce a range of fairly disparate problems to a set fairly narrow prototypes. Much of the science of machine learning is then solving those problems and providing good guarantees for the solutions.

Figure 1.

Machine learning techniques include both unsupervised and supervised learning

Machine Learning: Algorithms Types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:

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  Supervised Learning: The algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: The learner is required to learn (to approximate the behaviour of) a function which maps a vector into one of several classes by looking at several input-output examples of the function.

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  Unsupervised Learning: It models a set of inputs; labelled examples are not available.

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  Semi-Supervised Learning: It combines both labelled and unlabelled examples to generate an appropriate function or classifier.

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  Reinforcement Learning: The algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.

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  Transduction: Similar to supervised learning, but does not explicitly construct a function. Instead, it tries to predict new outputs based on training inputs, training outputs, and new inputs.

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  Learning to Learn: The algorithm learns its own inductive bias based on previous experience.

The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theory.

Machine learning is about designing algorithms that allow a computer to learn. Learning is not necessarily involves consciousness but learning is a matter of finding statistical regularities or other patterns in the data. Thus, many machine learning algorithms will barely resemble how human might approach a learning task. However, learning algorithms can give insight into the relative difficulty of learning in different environments.