Recurrent NN approach for FS using Cloud database.pptx

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1. A Recurrent Neural Network Approach for Feature Selection using Cloud Database

2. Introduction Combination of Cloud Computing, Neural Network and Feature Selection. Datasets stored in the cloud database are retrieved from the cloud environment train the data sets using feed-back neural network by applying proposed feature selection method to achieve the target concepts. better than the Conventional models handles noisy data and removes redundant, irrelevant features while considering feature interaction.

3. CLOUD COMPUTING delivery of computing as a service rather than a product On-demand self-service Broad Network access Resource pooling Measured Service

4. Cloud Deployment Model Private cloud Community cloud Public cloud Hybrid cloud Private cloud Community cloud Public cloud Hybrid cloud

5. Cloud Service Delivery Model Software as a Service ( SaaS ) Platform as a Service( PaaS ) Infrastructure as a Service( IaaS )

6. Data Mining Nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. Also known as KDD ( Knowledge Discovery in Databases )

7. Steps in KDD Data cleaning Data integration Data selection Data transformation Data mining Pattern evaluation Knowledge representation

8. Architecture of a typical Data mining System

9. Data Mining Techniques Association Classification Clustering Prediction Decision Trees Neural Networks

10. Data Preprocessing Incomplete, noisy, and inconsistent data are commonplace properties of large real world databases and data warehouses. Duplicate tuples also require data cleaning Need for data reduction A database/data warehouse may store terabytes of data Complex data analysis/mining may take a very long time to run on the complete data set

11. Data reduction strategies Data cube aggregation Dimensionality reduction — e.g., remove unimportant attributes Data Compression Numerosity reduction — e.g., fit data into models Discretization and concept hierarchy generation

12. Dimensionality Reduction Technique Principal component analysis Singular value decomposition Supervised and nonlinear techniques ( e.g., Feature Selection )

13. Feature Selection pre-processing step in data mining process which selects the most “relevant” subset of attributes according to some selection criteria. Two Commonly used approaches followed in feature selection are Wrapper approach Filter approach Wrapper approach is generally more accurate but also more computationally expensive.

14. Feature Selection Algorithm General Algorithm for Feature Selection Input: S- data sample with features X, |X| = n J – Evaluation measure to be Maximized GS – Successor generation Operator Output: Solution – (Weighted) feature subset L := Start_Point (X); Solution = { best of L according to J}; repeat L:= Search_Strategy ( L, GS(J),X); X ’ := { best of L according to J}; if J(X’)>=J(Solution) or (J(X’) = J(Solution) and |X’|< |Solution|) then Solution := X’; until Stop(J,L)  

15. Neural Network Neural computing requires a number of neurons, to be connected together into a neural network N eurons are arranged in layers Each neuron is a simple processing unit which takes one or more inputs and produces an output. At each neuron, every input has an associated weight which modifies the strength of each input . The neuron simply adds together all the inputs and calculates an output to be passed on.

16. Artificial neural network composed of many artificial neurons that are linked together according to specific network architecture. objective of the neural network is to transform the inputs into meaningful outputs. Inputs Output

17. Machine Learning type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. There are two types of learning Supervised learning Unsupervised learning two types of Supervised-Learning NNs Feed forward NNs Feed-back or recurrent NNs

18. Feed-back or recurrent NNs Supervised machine learning which has take over the advantage of processing the inputs and compares its resulting outputs against the desired outputs. Errors are then propagated back through the system, causing the system to adjust the weights which control the network. This process occurs over and over as the weights are continually tweak.