Survey on Classification Techniques for Data Mining
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TLDR
The different classification methods and classifiers that can be used for classification of observations that are initially uncategorized are compared to demonstrate the different accuracies and usefulness of classifiers.Abstract:
This paper focuses on the various techniques that can be implemented for classification of observations that are initially uncategorized. Our objective is to compare the different classification methods and classifiers that can be used for this purpose. In this paper, we study and demonstrate the different accuracies and usefulness of classifiers and the circumstances in which they should be implemented. General Terms Classification, Sentiment, Review, Accuracy, Positive, Negative, Neutral.read more
Citations
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Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform
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An FPGA-Based Hardware Accelerator for K-Nearest Neighbor Classification for Machine Learning on Mobile Devices
TL;DR: A unique, novel, and efficient hardware architecture to accelerate the K-nearest neighbor classifier on mobile devices, considering constraints associated with these devices, and evaluates the efficiency of the architecture, in terms of speedup, space, and accuracy.
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Comparative Evaluation of the Different Data Mining Techniques Used for the Medical Database
TL;DR: Three classification algorithms are used: J48 (an open source Java implementation of C4.5 algorithm), Multilayer Perceptron - MLP and Naïve Bayes (based on Bayes rule and a set of conditional independence assumptions) of the Weka interface to choose the best algorithm based on the conditions of the voice disorders database.
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Using machine learning to predict the driving context whilst driving
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References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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TL;DR: This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications and provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature.
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Support vector machines
TL;DR: This issue's collection of essays should help familiarize readers with this interesting new racehorse in the Machine Learning stable, and give a practical guide and a new technique for implementing the algorithm efficiently.
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Data Mining and Knowledge Discovery Handbook
Oded Maimon,Lior Rokach +1 more
TL;DR: This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently.
Proceedings Article
Twitter as a Corpus for Sentiment Analysis and Opinion Mining
Alexander Pak,Patrick Paroubek +1 more
TL;DR: This paper shows how to automatically collect a corpus for sentiment analysis and opinion mining purposes and builds a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document.
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