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Scientific Research Group in Egypt (SRGE),
http://www.egyptscience.net

Computational Model for Artificial
Learning Using ...
Agenda








2

Motivation
Contribution
Introduction
Background
 Classification Learning
 Formal Concept Analys...
Motivation
Artificial
intelligence
Embraces

Developing programs that
learn from past data

Understand mechanisms
embodied...
Motivation –Cont.
 Many applications have a huge amount of data.
civil registration record

 Unfortunately, the ability ...
Contribution
 We formulate a computational model for
 binary classification process using formal concept
analysis.
 The...
Introduction
 Machine learning is concerned (on the whole) with
concept learning and classification learning. The latter
...
Background
Classification Learning

 Generalize classes description by identifying the
common “core” characteristics of a...
Background
Formal Concept Analysis (1)
 Formal Concept Analysis (FCA) is a method used for
investigating and processing e...
Background
Formal Concept Analysis (2)
What is a Concept?
 A formal concept is constituted by two parts
A: set of
objects...
Background
Formal Concept Analysis (3)
Input

matrix specifying a set
of objects and attributes

Output

FCA

clusters of ...
Background
Mathematical Definition of FCA
 A formal concept is defined within a context.
Definition 1 A formal context is...
Background
Computational Models
 Assume that the human brain is an information
processing system and that thinking is a f...
The Proposed
Computational Model
 Induces the classification rules which characterize
each class.
 In the proposed model...
The Proposed
Computational Model
Input Data

training data and a partition
of the training set OC1 , OC2

Convert the give...
Experimental Results And
Discussions (1)

 The proposed model is applied to the following
datasets from the well-known UC...
Experimental Results And
Discussions (2)
 Some performance indices are calculated for the
proposed model such as the foll...
Experimental Results And
Discussions (3)
Data

Table II. Comparison of classification accuracy: SVM, CART and the
proposed...
Experimental Results And
Discussions (4)

Table III. Comparison of classification accuracy: SVM, CART and the
proposed mod...
Experimental Results And
Discussions (5)

Table IV. Comparison of performance indices for SVM, CART and the
proposed model...
Experimental Results And
Discussions (6)
Table VI. Comparison of performance indices for SVM, CART and the
proposed model ...
Experimental Results And
Discussions (7)
Table VIII. Comparison of performance indices for SVM, CART and the
proposed mode...
Experimental Results And
Discussions (8)

ROC curve for Monk1

22

ROC curve for Monk2

8th International Conference on Co...
Experimental Results And
Discussions (9)

ROC curve for Monk3

23

ROC curve for D1

8th International Conference on Compu...
Experimental Results And
Discussions (10)

ROC curve for D2
24

8th International Conference on Computer Engineering and S...
Conclusion
 Artificial learning is concerned with the classification
learning that is a supervised learning algorithm
emb...
Thank you

http://www.egyptscience.net
26

8th International Symposium Advances in Artificial Intelligence and Application...
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Computational model for artificial learning using formal concept analysis

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Transcript of "Computational model for artificial learning using formal concept analysis"

  1. 1. Scientific Research Group in Egypt (SRGE), http://www.egyptscience.net Computational Model for Artificial Learning Using Formal Concept Analysis Mona Nagy ElBedwehy Department of Mathematics, Faculty of Science, Damietta University Email: monanagyelbedwehy@ymail.com 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  2. 2. Agenda        2 Motivation Contribution Introduction Background  Classification Learning  Formal Concept Analysis (FCA)  Computational Models Proposed Computational Model Experimental Results and Discussions Conclusion 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  3. 3. Motivation Artificial intelligence Embraces Developing programs that learn from past data Understand mechanisms embodied in human and translating it into computer programs Artificial Learning 3 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  4. 4. Motivation –Cont.  Many applications have a huge amount of data. civil registration record  Unfortunately, the ability of understanding and using it does not keep track with its growth.  Methods to generate a “summary” that represent a conceptualization of the data set. similarities among different citizens (City=Cairo, Gender= Female)  Machine learning provides tools by which large quantities of data can be automatically analyzed to overcome these limitations and difficulties. Analysis of urban area population increase Marketing analysis of store departments Formal Concept Analysis is a technique that enables resolution of such problems. 4 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  5. 5. Contribution  We formulate a computational model for  binary classification process using formal concept analysis.  The classification rules are derived and applied successfully for different study cases. 5 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  6. 6. Introduction  Machine learning is concerned (on the whole) with concept learning and classification learning. The latter is simply a generalization of the former.  Classification permits predictions to be derived on the basis of common properties of a class of entities or phenomena.  We will concern on the second approach of the AL that is concerned on the classification learning. Classification learning is a learning algorithm for classifying unseen examples into predefined classes based on a set of training examples. 6 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  7. 7. Background Classification Learning  Generalize classes description by identifying the common “core” characteristics of a set of training objects to generate knowledge that will enable novel objects to be identified as belonging to one of the classes. Classification Learning Algorithms Statistical Classification Decision Trees Neural Networks Backpropagation CART 7 Prism SVM Naïve Bayes Bayesian Networks Symbolic algorithms The proposed model 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  8. 8. Background Formal Concept Analysis (1)  Formal Concept Analysis (FCA) is a method used for investigating and processing explicitely given information, in order to allow for meaningful and comprehensive interpretation.  Proposed by Wille.  An analysis of data.  Structures of formal abstractions of concepts of human thought.  Formal emphasizes that the concepts are mathematical objects, rather than concepts of mind. 8 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  9. 9. Background Formal Concept Analysis (2) What is a Concept?  A formal concept is constituted by two parts A: set of objects 9 Relations B: set of attributes  Having a certain relation  Every object belonging to this concept has all the attributes in B.  Every attribute belonging to this concept is shared by all objects in A.  A is called the concept's extent.  B is called the concept's intent. 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  10. 10. Background Formal Concept Analysis (3) Input matrix specifying a set of objects and attributes Output FCA clusters of attributes clusters of objects  Object cluster is the set of all objects that share a common subset of attributes.  Attribute cluster is the set of all attributes shared by one of the natural object clusters. Duck Goose Parrot … Object_1 Objects 10 Object_2 relation Has beak Has feather Has two legs … Attribute_1 Attribute_2 Attribute_3 Attributes 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  11. 11. Background Mathematical Definition of FCA  A formal concept is defined within a context. Definition 1 A formal context is (O, A, R) where O (objects) and A (attributes), and R is a binary relation between O and A.  Equation (1) represents the set of attributes common to the objects in M, while the set of objects which have all attributes in B is represented as in Equation (2). M '  a  A o R a for all o  M  (1) (2) B '  o  O o R a for all a  B Definition 2 A formal concept of the context (O, A, R) is a pair (M, B) of M  O , B  A , B’= M and M’=B. 11 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  12. 12. Background Computational Models  Assume that the human brain is an information processing system and that thinking is a form of computing.  Processes information by taking input and follows, a step-by-step algorithm to get a specific output.  The aim of computational modeling is to:  increase our knowledge.  improve our understanding of how the human brain works  build computer systems that can execute a given task optimally and in the most efficient possible way. 12 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  13. 13. The Proposed Computational Model  Induces the classification rules which characterize each class.  In the proposed model: 1. Convert the given data into a binary data. Binary data are data those unit can take on only two possible values termed 0 and 1. We do extension to the collection of attributes by new attributes to represent the binary data. 2. Use FCA to describe the classification process, so the following two functions are presented: R (o)  a  A|  o, a   R N  a   o  O |  o, a   R 13 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  14. 14. The Proposed Computational Model Input Data training data and a partition of the training set OC1 , OC2 Convert the given data into a binary data Compute k- Conjunction for A Add a to AC1 & R(a) to D C1 R(a)  OC1  ø R(a)  OC2= ø Add a to AC2 & R(a) to D C2 R(a)  OC1 = ø R(a)  OC2  ø Add R(a) with maximum no. of objects in Dci to FCi 14 Find R(a) If R(a)  O  ø add attribute to FCi, remove R(a) from O and Dci While DCi ø 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT While O  ø While last attribute in k- Conjunction is not reached
  15. 15. Experimental Results And Discussions (1)  The proposed model is applied to the following datasets from the well-known UCI repository of machine learning datasets that haven’t missing attributes. Table I. Datasets used in learning the concept classification Dataset Description No. of classes No. of attributes No. of instances monk1 Monk’s Problem1 2 6 432 monk2 Monk’s Problem2 2 6 432 monk3 Monk’s Problem3 2 6 432 2 6 120 2 6 120 D1 D2 Acute Inflammations(Inflammation of urinary bladder) Acute Inflammations (Nephritis of renal pelvis origin) Note: Monk3 problem contains 5% noise data. 15 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  16. 16. Experimental Results And Discussions (2)  Some performance indices are calculated for the proposed model such as the following, where TP (true positive), TN (true negative), FP (false positive), FN (false negative). TP TN Sensitivity  Recall  , Specificity  , TP  FN TN  FP Accuracy  TP  TN , TP  FP  TN  FN F  Measure  2( Precision  Recall ) , Precision  Recall TP PP  Precision  , TP  FP TN NP  TN  FN  The performance indices of the proposed model are compared with Support Vector Machine (SVM) and Classification and Regression Tree (CART). 16 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  17. 17. Experimental Results And Discussions (3) Data Table II. Comparison of classification accuracy: SVM, CART and the proposed model (P. model) SVM monk1 66.20% monk2 63.19% monk3 78.70% D1 100.0% D2 100.0% 17 Correct CART 83.33% 61.11% 97.22% 85.00% 100.0% Incorrect P. model 92.59% 63.66% 86.11% 100.0% 100.0% SVM CART P. model 33.80% 16.67% 0.00% 36.81% 38.89% 18.06% 21.30% 2.78% 7.18% 0.00% 15.00% 0.00% 0.00% 0.00% 0.00% Misclassified SVM 0.00% 0.00% 0.00% 0.00% 0.00% CART 0.00% 0.00% 0.00% 0.00% 0.00% 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT P. model 7.41% 18.28% 6.71% 0.00% 0.00%
  18. 18. Experimental Results And Discussions (4) Table III. Comparison of classification accuracy: SVM, CART and the proposed model (P. model) : misclassified assigned to majority class Dataset monk1 monk2 monk3 D1 D2 18 Correct Accuracy SVM CART P. model 66.20% 83.33% 100.0% 63.19% 61.11% 76.39% 78.70% 97.22% 87.27% 100.0% 85.00% 100.0% 100.0% 100.0% 100.0% Incorrect Accuracy SVM CART P. model 33.80% 16.67% 0.00% 36.81% 38.89% 23.61% 21.30% 2.78% 12.73% 0.00% 15.00% 0.00% 0.00% 0.00% 0.00% 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  19. 19. Experimental Results And Discussions (5) Table IV. Comparison of performance indices for SVM, CART and the proposed model for monk1 TP TN FP FN Sen. Spe. PP NP FM P. model 216 216 0 0 100% 100% 100% 100% 100% SVM 137 149 67 79 63.43% 68.98% 67.16% 65.35% 65.24% CART 168 192 24 48 77.78% 88.89% 87.50% 80.00% 82.35% Table V. Comparison of performance indices for SVM, CART and the proposed model for monk2 TP FP FN P. model 244 86 56 46 SVM 259 14 128 CART 19 TN 199 65 77 Sen. Spe. PP NP FM 84.14% 60.56% 81.33% 65.15% 82.71% 31 89.31% 9.86% 66.93% 31.11% 76.52% 91 68.62% 45.77% 72.10% 41.67% 70.32% 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  20. 20. Experimental Results And Discussions (6) Table VI. Comparison of performance indices for SVM, CART and the proposed model for monk3 TP TN FP FN P. model 199 179 49 5 SVM 157 183 45 47 CART 204 216 12 0 Sen. NP FM 97.55% 78.51% 80.24% 97.28% 88.05% 76.96% 80.26% 77.72% 79.57% 77.34% 100% 97.14% 100% Spe. PP 94.74% 94.44% Table VII. Comparison of performance indices for SVM, CART and the proposed model for inflammation of urinary bladder TP FP FN Sen. Spe. PP NP FM P. model 23 17 0 0 100% 100% 100% 100% 100% SVM 23 17 0 0 100% 100% 100% 100% 100% CART 20 TN 17 17 0 6 73.91% 100% 100% 73.91%% 85.00% 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  21. 21. Experimental Results And Discussions (7) Table VIII. Comparison of performance indices for SVM, CART and the proposed model for D2 TP TN FP FN Sen. Spe. PP NP FM P. model 17 23 0 0 100% 100% 100% 100% 100% SVM 17 23 0 0 100% 100% 100% 100% 100% CART 17 23 0 0 100% 100% 100% 100% 100%  ROC curve is a graphical plot that illustrates the performance of a binary classifier system.  ROC is created by plotting the fraction of true positives out of the positives (sensitivity) vs. the fraction of false positives out of the negatives (1-specificity) 21 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  22. 22. Experimental Results And Discussions (8) ROC curve for Monk1 22 ROC curve for Monk2 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  23. 23. Experimental Results And Discussions (9) ROC curve for Monk3 23 ROC curve for D1 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  24. 24. Experimental Results And Discussions (10) ROC curve for D2 24 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  25. 25. Conclusion  Artificial learning is concerned with the classification learning that is a supervised learning algorithm embodied in the human mind.  Proposed a computational model for classification learning process which is described in terms of formal concept analysis (FCA).  The proposed model characterizes each class and predict the class label of a new object.  The performance of the proposed model has been evaluated for the real world data which led to get on classification rules from the training data that enable us from predicting the outcome of unseen data in a test set.  The proposed model has superior performance comparing with CART and SVM. 25 8th International Conference on Computer Engineering and Systems (ICCES’2013), EGYPT
  26. 26. Thank you http://www.egyptscience.net 26 8th International Symposium Advances in Artificial Intelligence and Applications (AAIA'13)
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