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A-Class : a novel classification method

A-Class : a novel classification method. I.Tsoulos, A. Tzallas , E. Glavas. Presentation Layout . Data classification Grammatical evolution Mobile programming Implementation Experimental results Future work. 2. Data Classification.

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A-Class : a novel classification method

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  1. A-Class: a novel classification method I.Tsoulos, A. Tzallas, E. Glavas

  2. Presentation Layout • Data classification • Grammatical evolution • Mobile programming • Implementation • Experimental results • Future work 2

  3. Data Classification • Usedin chemistry, economics, physics, medicine etc. • Usuallythe data are divided into: • Train data: A dataset used for the training of the proposed method • Test data: The dataset where the proposed method will be evaluated • Example of methodsare: • K-nearest neighbours • Radial basis functions • Artificial neural networks • Support vector machines 3

  4. Grammatical Evolution • Genetic algorithm • Introducedby Ryan and O'Neil • It has been used in many scientific & practical applications • It requires: • The grammar of the target problem in BNF notation • An associated fitness function • Our case… • the fitness is the classification error from the application of the produced rules upon to the training set • Genetic Evolution is only used to transform a typical chromosome into human readable programme M. O’Neill, C. Ryan, Grammatical evolution, IEEE Trans. Evol. Comput. 5 (2001) 349–358 4

  5. Mobile programming • Our study is designed... not only in desktop environments • ... can be executed in recent mobile devices • Many programming languages: • Java for Android • Objective C for Iphone • C# for Windows Phones • Javascript for Firefox OS 5

  6. Implementation • Use of QtCreator • Utilization of C++ language • Freely available from http://qt-project.org • It can be installed in any operation system • It can produce mobile applications for Android & IOS • This means… • We write our program once & the produced output can be run in any mobile device • We can produce executables with the same source code in any desktop environment 6

  7. QtCreator Environment 7

  8. Algorithm Description • Read the train data of the problem • Random initialization of the chromosomes • For a number of generations Do • Fitness evaluation • Create a new genetic population using mutation & crossover • End-For • Create a classification program induced by the best chromosome in the population • Apply the above program to the test set 8

  9. Experimental setup • Two (2) Datasets from UCI Repository • Wine • Glass • One (1) artificial dataset (Circular) • Two fold Experiments (50 % train and 50% testing) • 30 individual runs for every dataset & averages are taken 9

  10. Typical output of the software • if(x9>exp((947.6-(x13*log(x12))))) CLASS=0.00 else if(x10<=((-306.79)/(-87.77))|x7>=exp(x8)) CLASS=1.00 else CLASS=2.00 Output for a random generation for the dataset wine 10

  11. Screenshot of the execution of the method 11

  12. Results (1/2) Experiments using different number of generations & fixed size of chromosomes to 200 12

  13. Results (2/2) More experiments were conducted using fixed number of generations (set 500) & different number of chromosomes 13

  14. Conclusions • A novel method for classification problems • …utilizes the Grammatical Evolution procedure to create classification programs expressed in a C – like programming language • ….was tested on a series of well known problems • The associated software was implemented using Qt Creator programming environment & was installed on Android mobile devices 14

  15. Future Work • The software can be extended in the following ways: • Implementation & inclusion of a better stopping rule • Currently, the software terminates using a maximum number of generations • This is not efficient & it can consume the battery of the mobile device very fast in some cases • Addition of a new button to access program settings • Support a better mechanism of fetching datasets • Application to real world problems from areas such as medicine & economics 15

  16. Thank you!!! 16

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