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School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus A-Class: a novel classification method I.Tsoulos, A. Tzallas, E. Glavas.

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Presentation on theme: "School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus A-Class: a novel classification method I.Tsoulos, A. Tzallas, E. Glavas."— Presentation transcript:

1 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus A-Class: a novel classification method I.Tsoulos, A. Tzallas, E. Glavas

2 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Presentation Layout  Data classification  Grammatical evolution  Mobile programming  Implementation  Experimental results  Future work 2

3 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Data Classification  Used in chemistry, economics, physics, medicine etc.  Usually the 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 methods are:  K-nearest neighbours  Radial basis functions  Artificial neural networks  Support vector machines 3

4 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Grammatical Evolution  Genetic algorithm  Introduced by 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 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 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 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus 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 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Implementation  Use of QtCreator  Utilization of C++ language  Freely available from  It can be installed in any operation system  It can produce mobile applications for Android & IOS 6 This means… We write our program once & the produced output can be run in any mobile device 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 We can produce executables with the same source code in any desktop environment

7 QtCreator Environment 7

8 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Algorithm Description 1.Read the train data of the problem 2.Random initialization of the chromosomes 3.For a number of generations Do Fitness evaluation Fitness evaluation Create a new genetic population using mutation & crossover Create a new genetic population using mutation & crossover 4.End-For 5.Create a classification program induced by the best chromosome in the population 6.Apply the above program to the test set 8

9 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus 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 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus if(x9>exp((947.6-(x13*log(x12))))) CLASS=0.00 else if(x10 =exp(x8)) CLASS=1.00 else CLASS=2.00 if(x9>exp((947.6-(x13*log(x12))))) CLASS=0.00 else if(x10 =exp(x8)) CLASS=1.00 else CLASS=2.00 Typical output of the software Output for a random generation for the dataset wine 10

11 Screenshot of the execution of the method 11

12 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Results (1/2) DATASETGENERATIONS TEST ERROR WINE % WINE % WINE % GLASS % GLASS % GLASS % CIRCULAR % CIRCULAR % CIRCULAR % Experiments using different number of generations & fixed size of chromosomes to

13 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Results (2/2) DATASETCHROMOSOMES TEST ERROR WINE % WINE % WINE % GLASS % GLASS % GLASS % CIRCULAR % CIRCULAR % CIRCULAR % More experiments were conducted using fixed number of generations (set 500) & different number of chromosomes 13

14 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus 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 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus 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 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 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 School of Applied Technology, Dep. Of Computer Engineering, T.E.I of Epirus Thank you!!! 16


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