SIT717 – Enterprise Business Intelligence | Supervised Learning
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Deakin University
SIT717 Enterprise Business Intelligence (SIT717)
Added on 2020-03-04
SIT717 – Enterprise Business Intelligence | Supervised Learning
Deakin University
SIT717 Enterprise Business Intelligence (SIT717)
Added on 2020-03-04
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SUPERVISED LEARNING1SUPERVISED LEARNING
SUPERVISED LEARNING2AbstractMachine learning is a field of Artificial intelligence which imparts the computer applications theability to learn from the inputs. The field of machine learning deals with the study of algorithmswhich have the ability to learn from the training or historical data and make predictions orclassification of that input data by building a model from the sample inputs. Due to their abilityto learn from the input data, classification and prediction machine learning has a wide range ofapplication from pattern recognition, speech recognition, medical diagnosis applications,intrusion detection systems, congestion detection systems, optical character recognition,computer vision etc. Generally, the machine learning methods are classified as supervisedlearning, unsupervised learning, and reinforcement learning methods. Supervised learningmethod system is presented with a set of input condition and an instructor providing the desiredoutput, thus making the system understand what to expect for an answer in a certain scenario,provides the desired outputs. The supervised learning method is the most popular machinelearning method due to its ease of use and flexibility it offers. This work explores supervisedlearning in more detailed manner. Keywords: Machine learning, Supervised learning.
SUPERVISED LEARNING3Table of ContentsAbstract............................................................................................................................................2Introduction......................................................................................................................................4Supervised learning.....................................................................................................................5Supervised learning model and algorithm...............................................................................6Issues to be considered in supervised learning........................................................................8Algorithm of supervised learning................................................................................................9Applications of supervised learning..............................................................................................15Conclusion.....................................................................................................................................16References......................................................................................................................................17
SUPERVISED LEARNING4IntroductionMachine Learning (ML) could be termed as a branch of Artificial Intelligence, as they containthe methods, which allows the computers and systems to act more smartly. The result is a uniqueway of cumulative working of various functions in an orderly manner, rather than simple datainsertion and retrieval from applications like database and others, making the machines takebetter decision are different situations with minimal input from the user. Machine learning is abranch of study, grouped from various different fields, such as computer science, statistics,biology, and psychology. The base functionality of Machine Learning is to identify the bestPredictor model for making decisions, by analyzing and learning from previous scenarios, whichis the job of a Classifier. The job of Classification is the prediction of unknown scenarios(output) by analyzing known scenarios (input). The process of classification is performed overdata set D comprising of the following objects: Let the set size be {X1, X2, |X|} where |X| signifies the attributes count of the set XClass label is Y then the target attribute; ? y1, y2, |Y|} where Y is the number of classes and Y 2.Then the basic goal of the machine learning is prediction or classification over the dataset D,such that it relates the attributes in X and classes in Y (Mohri et al, 2012).Classification of Machine Learning is on the basis of the type of input signal or feedbackreceived by the learning system. These are as follows:
SUPERVISED LEARNING5Supervised learning: System is presented with a set of input condition and an instructorproviding the desired output, thus making the system understand what to expect for an answer ina certain scenario, provides the desired outputs (Zhang et al, 2011).Unsupervised learning: There are no markers in the learning methodology, thus stranding thesystem to discover patterns in the inputs. This type of learning can become a target in itself(unraveling pattern in data) or the end game (feature learning).Reinforcement learning: System is challenged with an unprecedented environment to perform(for instance driving or playing against an opponent). The system is either rewarded orpunished, based on the performance it displayed while navigating in the problem sphere.This paper deals with Supervised Learning, the various processes and all the functionalities.Supervised learningThis type of learning is based on understanding the mapping of certain attributes and functions ina predefined scenario, then using the knowledge gathered in this process to make a decisionbased on the mapping learned, in unprecedented scenarios. This way of learning is veryimportant and is a key functionality in multimedia processing (Mohri et al, 2012).In supervised learning a computer model represents a learner system which contains two set ofdata namely the training data set and the other set is the testing data set. For example consider asystem for classification of a particular disease. In such scenario a system will use a data setwhich contains records of the patients with their diseases. This record is split into training dataset and the testing data set. The idea behind this type of learning is to train the learner systemwith all possible outcomes in the training set, so it can perform with the highest percentage ofaccuracy in the test set. Which means, the target of a learner is to make out the pattern in the
SUPERVISED LEARNING6inputs provided in the test set and find a solution from what it has learned in the training set? Theclassification then can be the categories of diseases for the given example. Similarly the trainingset might include pictures of dogs like terrier and spaniel, along with the identification of each,now the test set would include another group of unidentified images but of the same set. Thetarget for the learner is to design a rule to guide itself towards a solution in un-known scenarios(Mohri et al, 2012).In supervised learning the training set comprises of various ordered pairs like (a1, b1), (a2,b2), ...,(an, bn), where each ai is represents the set of measurements of a single example datapoint, and bi is the label for that data point. Consider an example where, a ai might be a group offive attributes for a cricket match, such as run-rate, wickets in hand, strike rate, fielding plan andindividual performance. In such case the corresponding bi would be a classification of the gameas a ‘win” or “loose”. Generally a testing data is comprises of data but without labels: (an+1,an+2... an+m). As discussed earlier, the target is to make an educated guess in the test set about“win” or “loose” by using the learning achieved in training set (Mohri et al, 2012).Supervised learning model and algorithmFollowing are the steps performed in order to solve a problem of Supervised Learning:1.Classifying the type of training set. Before proceeding further, an engineer must decidewhat type of training set he must use for his system. It could be a single unit, a group of itor a bunch of it (Rambhajani et al.,2015).2.Collect a set. The training set should model the real world entities, so a training set isgathered according. Along with this, possible outcomes are collected to form a set, eitherthrough experience or through some empirical measurements (Mohri et al, 2012).
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