Supervised Machine Learning Approaches: A Survey (original) (raw)

Supervised Learning Classification

Learning is a way to develop the skills and knowledge. It is a fundamental property of our brain to acquire the new knowledge and to develop new skill also. The type of learning we have included in our paper are Machine Learning, supervised Learning, and classification of supervised learning. It includes many things about machine learning like their advantages, disadvantages and applications of machine learning (like virtual personal assistance, online media services, E-mail spam). Types of ML included supervised learning, unsupervised learning, and reinforcement learning. There are many SL algorithms which are useful for determining the accuracy of the program but in some case there may be an issues that may occur with supervised learning as we will discuss below in the paper. Algorithm may be used for the determination of accuracy, prediction as well as for better analyses. We use Support vector machine for minimizing the upper bound generalization error. These are directed learning models with related learning calculations that examine data utilization for classification and relapse examination, One another classification method belong to the same family called as Naïve Bayesian network. It basically works on Bayes theorem, it shoulders that the occurrence of the selected features in very category is distinct to the existence of the further attribute. Another supervised technique is Decision Tree in which it identifies the no. of ways to split data based on different condition. The decision tree it divided into two nodes decision node and leaf node each node have different feature and function discussed in below in the paper. The last technique we have discussed is KNN (k-nearest neighbour) in which it determines how many neighbours are to be placed in a single class. We composed the comparison chart on the basis of best algorithm with their accuracy.

Supervised Machine Learning: A Review of Classification Techniques

Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.

A Survey on Machine Learning: Concept,Algorithms and Applications

International Journal of Innovative Research in Computer and Communication Engineering, 2017

Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered computational approaches, to an independent research discipline that has not only provided the necessary base for statistical-computational principles of learning procedures, but also has developedvarious algorithms that are regularly used for text interpretation, pattern recognition, and a many other commercial purposes and has led to a separate research interest in data mining to identify hidden regularities or irregularities in social data that growing by second. This paper focuses on explaining the concept and evolution of Machine Learning, some of the popular Machine Learning algorithms and try to compare three most popular algorithms based on some basic notions. Sentiment140 dataset was used and performance of each algorithm in terms of training t...

Identifying the Machine Learning Techniques for Classification of Target Datasets

Sukkur IBA Journal of Computing and Mathematical Sciences, 2020

Given the dynamic and convoluted nature of numerous datasets, the necessity of enhancing performance outcomes and handling multiple datasets has become more challenging. To handle these issues effectively and improve the quality of multiple approaches, the capabilities of various Machine Learning techniques such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes(NB) and Support Vector Machine (SVM) have been utilized in this study. In this paper, the binary classification method using five different datasets, and many predictor variables have been utilized. Moreover, this research has mainly focused on determining the classification of data into the subsets that share the standard designs. In this regard, many approaches had been studied extensively and used to achieve better yields from the existing literature; however, they were inadequate to provide efficient outcomes. By applying four Supervised ML classification algorithms along with the UCI Datasets of ML Repository, the robustness of the method is progressed. The proposed mechanism is assessed by adopting five performance criteria concerning the accuracy, AUC (Area Under Curve), precision, recall, and F-measure values. The current study experimental results revealed that there is a significant improvement in the confusion matrix rate compared with a similar study and this method can also be used for machine learning problems such as binary classification.

A Survey on Supervised Classification Techniques in Machine Learning

2017

The recent research is going on Machine Learning (ML). It has evolved from the attempt of few computer scientists exploiting the possibility of computers learning to play games, and a part of statistics that not often considered computational approaches. There also has developed various algorithms that are regularly used for image segmentation, text interpretation, pattern recognition, and a many other commercial purposes and has led to a separate research interest in Big data and data mining to identify hidden regularities or irregularities in social data that growing fast. This paper focuses on the literature of supervised learning techniques in Machine Learning. Some of the popular Supervised Machine Learning algorithms and try to compare most popular algorithms based on some basic notions.

Conceptual Review on Machine Learning Algorithms for Classification Techniques

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

Machine leaning is a ground of recent research that officially focuses on the theory, performance, and properties of learning systems and algorithms. It is a extremely interdisciplinary field building upon ideas from many different kinds of fields such as artificial intelligence, optimization theory, information theory, statistics, cognitive science, optimal control, and many other disciplines of science, engineering, and mathematics. Because of its implementation in a wide range of applications, machine learning has covered almost every scientific domain, which has brought great impact on the science and society. It has been used on a variety of problems, including recommendation engines, recognition systems, informatics and data mining, and autonomous control systems. This research paper compared different machine algorithms for classification. Classification is used when the desired output is a discrete label.

A Comparative Study on Supervised Machine Learning Algorithm

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Machine learning enables computers to act and make data driven decisions rather than being explicitly programmed to carry out a certain task. It is a tool and technology which can answer the question from your data. These programs are designed to learn and improve over time when exposed to new data. ML is a subset or a current application of AI. It is based on an idea that we should be able to give machines access to data and let them learn from themselves. ML deals with extraction of patterns from dataset, this means that machines can not only find the rules for optimal behavior but also can adapt to the changes in the world. Many of the algorithms involved have been known for decades. In this paper various algorithms of machine learning have been discussed. Machine learning algorithms are used for various purposes but we can say that once the machine learning algorithm studies how to manage data, it can do its work accordingly by itself.

Supervised Machine Learning Algorithms: Classification and Comparison

Supervised Machine Learning (SML) is a search for algorithms that cause given external conditions to produce general hypotheses, and then make predictions about future events. Supervised classification is one of the most frequently performed tasks by smart systems. This paper describes various Supervised Machine Learning (ML) methods for comparing, comparing different learning algorithms and determines the best-known algorithm based on the data set, number of variables and variables (features). : Decision Table, Random Forest (RF), Naive Bayes (NB), vector Support Machine (SVM), Neural Networks (Perception), JRip and Tree Decision (J48) using learning tool the Waikato Information Machine (WEKA). In order to use algorithms, diabetes data were set up to be classified into 786 cases with eight characteristics such as independent variables and reliability analyzes. The results indicate that the SVM was found to be an algorithm with great accuracy and accuracy. Naive Bayes and Random Forest classification algorithms were found to be more accurate following SVM. Studies show that the time it takes to build a model and accuracy (accuracy) is a factor on the other hand; while statistical kappa and mean Absolute Error (MAE) are another factor on the other hand. Therefore, ML algorithms require more precision, accuracy and less error to evaluate machine learning prediction.

Supervised Learning: Classification and Comparison

GRD Journals , 2020

Supervised Machine Learning Approaches: A Survey (original) (raw)

Related papers