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Classification Methods in Data Mining:A Detailed Survey
TLDR
The goal of this survey is to provide a detailed review of two classification techniques and its applications in various emerging fields and the comparison of these techniques that are being widely used.Abstract:
Classification is a data mining (machine learning) technique and a model finding process that is used for assigning the data into different classes according to specific constrains. In other words we can say that classification is used to predict group membership for data instances. There are several major kinds of classification alg orithms including Genetic algorithm C4.5, NaOve Bayes, SVM, KNN, decision tree, Neural Network and CART. The goal of this survey is to provide a detailed review of two classification techniques and its applications in various emerging fields. This paper al so presents the comparison of these techniques that are being widely used.read more
Citations
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How to adjust an ensemble size in stream data mining
TL;DR: A new approach for designing an ensemble applied to stream data classification is proposed, based on the observation that probability of the correct tree outcome is different in various tree sections and a novel procedure of weighting ensemble components, i.e. decision trees, by assigning a weight to each leaf of the tree.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
Neurons with graded response have collective computational properties like those of two-state neurons.
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Book
Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
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Artificial neural networks and their business applications
TL;DR: The paper reviews the common characteristics of neural networks and discusses the feasibility of neural-nets applications in business fields, then presents four actual application cases and identifies the limitations of the current neural-net technology.
Journal ArticleDOI
Applications of neural networks to character recognition
TL;DR: A general introduction to neural network architectures and learning algorithms commonly used for pattern recognition problems is given and the design of a neural network character recognizer for on-line recognition of handwritten characters is described in detail.