Comparing Different Machine Learning Techniques for Classifying Multi Label Data

In recent years, multi-label classifications have become common. Multi label classification is a classification in which a collection of labels is associated with a single instance, which may be a variation of the classification of a single label. The problem of huge data is the classification in which each instance is of different kind which further can be identified with more than one class. The various machine learning strategies for classifying multi-label data are discussed in this paper. Many researches have been carried out that specify the grouping of multiple labels. Here we will compare various classification machine learning techniques that involve two approaches: the adapted algorithm approach and the method of problem transformation. Here we are using naive multinomial bayes and logistic regression. We use certain evaluation metrics to predict the differences as well. Better classification methods are discussed in this paper.

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Automatic classification of sources in large astronomical catalogs

Proceedings of the International Astronomical Union ◽

10.1017/s1743921319002576 ◽

2019 ◽

Vol 15 (S341) ◽

pp. 109-113

Author(s):

Agnieszka Pollo ◽

Aleksandra Solarz ◽

Małgorzata Siudek ◽

Katarzyna Małek ◽

Maciej Bilicki ◽

...

Keyword(s):

Machine Learning ◽

Large Data ◽

Machine Learning Techniques ◽

Classification Methods ◽

Classification Schemes ◽

Astronomical Catalogs ◽

Learning Techniques ◽

Sky Survey ◽

Optimal Classification

AbstractIn this paper we address two questions related to data analysis in large astronomical datasets, and we demonstrate how they can be answered making use of machine learning techniques. The first question is: how to efficiently find previously unknown or rare objects which can be expected to exist in big data samples? Using the largest existing extragalactic all-sky survey, provided by the WISE satellite, we demonstrate that, surprisingly, supervised classification methods can come to aid. The second question is: having a sufficiently large data sample, how can we look for new optimal classification schemes, possibly finding new and previously unknown classes and subclasses of sources? Based on the VIPERS cutting-edge galaxy catalog at redshift z > 0.5, we demonstrate that unsupervised classification methods can give unexpected but physically well-motivated results.

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A Brief Survey on Text Classification Using Various Machine Learning Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i1.521 ◽

2018 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Padmavathi .S ◽

M. Chidambaram

Keyword(s):

Machine Learning ◽

Text Classification ◽

Fixed Number ◽

Machine Learning Techniques ◽

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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