Classification Methods

Classification is a data mining technique based on machine learning which is used to classify each item in a set of data into a set of predefined classes or groups. Classification methods make use of mathematical and statistical techniques such as decision trees, linear programming, neural network and other methods like Genetic Approach, Fuzzy set Approach, Ruled based etc. This paper is an survey of different classification methods and there advantages. In this paper Classification Method is considered, it focuses on a survey on various classification techniques that are most commonly used in data-mining.

Template based Course Assignment (Not published in the Journal of Machine Learning)

This report describes in a comprehensive manner the various types of classification algorithms that already exist. I will mainly be discussing and comparing in detail the major 7 types of classification algorithms here. The comparison will essentially be based on objective function, assumptions, advantages and draw backs of each.

2009

Using classification algorithms can lead to discovering relevant knowledge contained in educational databases. These findings can be used for providing feedback to learners in the educational process.

Classification is a widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. Classification is a supervised learning technique in data mining where training data is given to classifier that builds with classification rules. Later if test data is given to classifier, it will predict the values for unknown attributes. Classification consists of predicting a certain outcome based on a given input. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome usually called prediction attribute. The algorithm tries to discover relationships between the attributes that would make it possible to predict the outcome. In order to predict the things correctly, attribute construction is needful for solving the problems. Hence, the motivation of this study is to propose a new attribute generation approach to extend a small data set into a purpose oriented and a higher dimension feature space to enhance the classification accuracy. This paper reviews all the data mining techniques especially classification domain for attribute construction.

In classification, no prior information is required for predicting the class label. A classification technique is an organized approach for building classification model from a given input dataset. The learning algorithm of each technique is employed to build a model used to find the relationship between attribute set and class label of the given input data. Various classification techniques used are Decision Tree, Naïve Bayes, and Nearest Neighbour. k- Nearest Neighbour is one of simple and well known classification technique in which distance is measured between input point and all other records of the dataset. The class label of the k-Nearest Neighbour is the class label for input point. The objective of this paper is to understand and analyze various classification techniques used in data mining.

Advances in Computational Intelligence and Robotics, 2017

Learning is the ability to improve behavior based on former experiences and observations. Nowadays, mankind continuously attempts to train computers for his purpose, and make them smarter through trainings and experiments. Learning machines are a branch of artificial intelligence with the aim of reaching machines able to extract knowledge (learning) from the environment. Classical, fuzzy classification, as a subcategory of machine learning, has an important role in reaching these goals in this area. In the present chapter, we undertake to elaborate and explain some useful and efficient methods of classical versus fuzzy classification. Moreover, we compare them, investigating their advantages and disadvantages.

Lecture Notes in Computer Science, 2000

Classification is a central concept in object-oriented approaches such as object-oriented programming, object-oriented knowledge representation systems (including description logics), object-oriented databases, software engineering and information retrieval. Nevertheless, research works on classification have often been carried out separately within these different approaches, and they have not always been precisely confronted and connected. The goal of the workshop was to confront these complementary viewpoints on classification, to exhibit and discuss commonalities and differences within these approaches.

International Journal of Computer Applications, 2017

Data Mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful informationmaking it more accurate, reliable, efficient and beneficial. In data mining various techniques are used-classification, clustering, regression, association mining. These techniques can be used on various types of data; it may be stream data, one dimensional, two dimensional or multi-dimensional data. In this paper we analyze the data mining techniques based on various parameters. All data mining techniques used in various fields for prediction and extraction of useful data or knowledge from a large data base is analyzed and each data mining technique has different performance. General Terms Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. classification is a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. A classification system is an approach to accomplishing classification.

This paper deals with techniques and algorithms of data classification. It describes the process of preparing the data and main ideas of four well-known algorithms -Decision tree induction,

Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification purpose. In this paper, we present the basic classification techniques. Later we discuss some major types of classification method including Bayesian networks, decision tree induction, k-nearest neighbor classifier and Support Vector Machines (SVM) with their strengths, weaknesses, potential applications and issues with their available solution. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning. This work will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods.