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Applying Fuzzy Theory in Intelligent Web Systems

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Title: Applying Fuzzy Theory in Intelligent Web Systems


1
Applying Fuzzy Theory in Intelligent Web
Systems
  • Chih-Ming Chen (???)
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2
Outline
  • The Used Fuzzy Concept
  • Fuzzy Inference
  • Fuzzy Association Rule
  • Neuro-fuzzy network
  • Current Researches Relating to Fuzzy Theory in
    Intelligent Web-based Learning Systems
  • Personalized Mobile Learning System based on
    Fuzzy Item Response Theory for Promoting English
    Vocabulary and Reading Abilities
  • Mining Formative Evaluation Fuzzy Rules Using
    Learning Portfolios for Web-based Learning
    Systems
  • The Other Intelligent Systems
  • News Archive and Data Mining Agent System
  • Chinese Word Segmentation System with Intelligent
    New Word Extension
  • Q A Time

3
Fuzzy Inference (1/3)
???????? (??) ???? (??) ??? (??)
???? Fuzzy???? (??) ????? (??) ????????? (??)
??????
4
Fuzzy Inference (2/3)
(facts) X is (rule) if X is A then Y is
B ???????? (result) Y is
Mamdani ?
A
B
1
1
0
0
5
Fuzzy-logic inference system (3/3)
6
Fuzzy Association Rule (1/8)
  • The KDD (Knowledge discovery in database) process
    generally consists of the following three phase
  • (1) Pre-processing.
  • (2) Data-mining.
  • (3) Post-processing.
  • Fuzzy Transaction Data-mining Algorithm (FTDA)
  • This is integrates fuzzy-set concepts with the
    apriori algorithm and uses the result to find
    interesting item-sets and fuzzy association
    rules.

7
Fuzzy Association Rule (2/8)
An example for the detailed process of Apriori
algorithm
8
Fuzzy Association Rule (3/8)
An example for mining fuzzy association rule
Case OOP DB ST DS MIS
1 86 77 86 71 68
2 61 87 89 77 80
3 84 89 86 79 89
4 73 86 79 84 62
5 70 85 87 72 79
6 65 67 86 61 87
7 71 87 75 71 80
8 86 69 64 84 88
9 75 65 86 86 79
10 83 68 65 85 89
9
Fuzzy Association Rule (4/8)
The defined fuzzy membership function for mining
fuzzy association rule
10
Fuzzy Association Rule (5/8)
11
Fuzzy Association Rule (6/8)
Case OOP.Middle DB.High OOP.MiddlenDB.High
1 0.0 0.0 0.0
2 0.0 0.8 0.0
3 0.1 0.9 0.1
4 1.0 0.7 0.7
5 0.7 0.6 0.6
6 0.2 0.0 0.0
7 0.8 0.8 0.8
8 0.0 0.0 0.0
9 0.8 0.0 0.0
10 0.2 0.0 0.0
Count 3.8 3.8 2.2
12
Fuzzy Association Rule (7/8)
13
Fuzzy Association Rule (8/8)
Itemset Itemset Count
OOP.Middle DB.High 2.2
OOP.Middle ST.High 1.8
OOP.Middle DS.Middle 1.7
OOP.Middle MIS.High 0.9
DB.High ST.High 2.2
DB.High DS.Middle 2.7
DB.High MIS.High 1.4
ST.High DS.Middle 2.8
ST.High MIS.High 1.8
DS.Middle MIS.High 1.1
14
Fuzzy-neuro Network
Four-layer learning architecture of the
neuro-fuzzy networks
15
Current Researches Relating to Fuzzy Theory in
Intelligent Web Systems
  • Personalized Mobile Learning System based on
    Fuzzy Item Response Theory for Promoting English
    Vocabulary and Reading Abilities

16
Introduction
  • The learning form is dramatically changing
  • E-learning (Electronic-learning)
  • M-learning (Mobile-learning)
  • U-learning (Ubiquitous-learning)
  • Mobile learning
  • an effective form of flexible learning
  • utilizing spare time for learning
  • learning takes place anytime, anywhere

17
Introduction
  • English is an international language.
  • English as Second Language, ESL
  • English as Foreign Language, EFL
  • How to learn English well ?
  • assistant tools
  • good material
  • little and often

18
Purpose of the Study
  • Considering the advantages of the mobile learning
  • Breaking the limitations of time and space
  • Utilizing spare time for learning
  • A personalized intelligent m-learning system
    (PIMS) for supporting effective English learning

19
System Design
  • A Personalized Intelligent M-learning System
    (PIMS) includes
  • The remote courseware server
  • The client mobile learning system
  • The feature of PIMS
  • Portable
  • Personalization
  • Intelligent tutoring system

20
PIMS System Architecture
21
System Architecture of Personalized Vocabulary
Learning System
22
The Remote Courseware Server
  • English news crawler agent
  • automatically retrieve English News from the
    Internet
  • Difficulty assessment agent of English news
  • automatically measuring the difficulty parameters
    of English news articles
  • Courseware management agent
  • online courseware management

23
The Client Mobile Learning System
  • Learning interface agent
  • providing a flexible learning interface
  • Feedback agent
  • collecting learner explicit feedback information
  • Personalized courseware recommendation agent
  • recommending a personalized courseware
  • evaluating learners reading ability

24
The Client Mobile Learning System
  • Personalized vocabulary recommendation agent
  • enhancing learner vocabulary ability
  • discovering the new vocabularies to individual
    learners

25
The Learning Procedure of the Client Mobile
Learning System
26
English E-News Archive
  • FTV English e-news
  • Metadata extraction mechanism
  • English and Chinese news titles
  • URL address
  • Date
  • News body

27
The detailed procedures of English news archive
28
Measuring Difficulty of English News Article
  • Readability
  • Flesch reading ease formula, 1948
  • The drawback of Flesch formula
  • No consideration of the readers vocabulary
    ability
  • Modified Flesch reading ease formula
  • Flesch RE
  • Proposed fuzzy difficulty parameter

29
Flesch Reading Ease Formula
  • Fleschs reading ease formula can be formulated
    as follows
  • RE represents the reading ease value ,range from
    0 (difficulty)100(easy)
  • ASL is the average sentence length
  • ASW is the average number of syllables per word

30
The Proposed Scheme for Evaluating Difficulty of
English News Article
  1. Computing the Percentages of Vocabulary
  2. Determining Fuzzy Membership Functions by the
    K-means Clustering Algorithm
  3. Designing Fuzzy Rule Base
  4. Fuzzy Inference

31
The determined fuzzy membership functions for the
percentage of occurring vocabulary of the
elementary level
32
The defined membership functions for the
difficulty of English News
33
The fuzzy rule base designed by English course
experts for inferring difficulty of English news
article
34
Defuzzification
The center of gravity (COG)
35
An Example for Inferring the Difficulty of
English News Article
36
Computing the value of English news by Fleschs
reading ease formula
Step1
Normalizing the RE value
Step2
37
Computing the difficulty of English news by fuzzy
inference
Step3
The triggered consequent parts of output variable
for defuzzification
38
Determining the final difficulty of English news
article by integrating the normalized and the
inferred difficulty values under the adjustable
weight is set to 0.5
Step4
39
Personalized English News Recommendation
  • Item Response Theory (IRT)

40
Personalized English News Recommendation
  • Evaluating English reading ability
  • the Bayesian estimation approaches is applied in
    this study

41
Personalized English News Recommendation
  • Recommending English news
  • the maximum information strategy

42
Personalized English News Recommendation
  • The drawbacks of item response theory
  • Learners response is not usually belonging to
    completely understanding or not understanding
    case for the content of learned courseware
  • The traditional item response theory cannot
    estimate learner ability for personalized
    learning services according to learners
    non-crisp responses (i.e. uncertain/fuzzy
    responses)

43
Personalized English News Recommendation
  • Fuzzy Item Response Theory (FIRT)

44
Personalized English News Recommendation
  • The designed fuzzy rule base for inferring
    learners understanding degree

45
Personalized English Vocabulary Recommendation
  • Personalized English Vocabulary Learning System
  • Learners vocabulary ability
  • Vocabulary difficulty parameter
  • Rij the set of the recommended new vocabularies
  • Ai the set of vocabularies that the
    corresponding difficulty parameters are higher
    than learners vocabulary ability
  • Cj the set of all vocabularies contained in the
    English news article
  • Li the set of the acquired vocabularies of the
    learner

46
The Implemented System (1/8)
(b)
(a)
47
The Implemented System (2/8)
(d)
(c)
48
The Implemented System (3/8)
(f)
(e)
49
The Implemented System (4/8)
(h)
(g)
50
The Implemented System (5/8)
(j)
(i)
51
The Implemented System (6/8)
(l)
(k)
52
The Implemented System (7/8)
(n)
(m)
53
The Implemented System (8/8)
(p)
(o)
54
The English Courseware Management System
55
The English Courseware Management System
56
The English Courseware Management System
57
The English Courseware Management System
58
Location-based English Learning System
59
Current Researches Relating to Fuzzy Theory in
Intelligent Web Systems
  • Mining Formative Evaluation Fuzzy Rules Using
    Learning Portfolios for Web-based Learning
    Systems

60
Research background
  • Learning performance assessment
  • To evaluate what learners learnt during the
    learning process.
  • The learning performance evaluation instruments
    could be classified as
  • the summative evaluation examination
  • the formative evaluation learning portfolio

61
Research background (cont.)the summative
evaluation v.s. the formative evaluation.
  • The summative evaluation
  • The traditional summative evaluation through
    performing examinations or feedback forms to
    evaluate the learning performance for both the
    conventional classroom learning and web-based
    learning.
  • To consider final learning outcomes without
    considering the learning process of learners.
  • The formative evaluation
  • By the formative assessment, teachers feed
    information back to students in ways that enable
    students to learn better, or when students can
    engage in a self-reflective process
  • The traditional portfolio v.s. the web-based
    learning portfolio
  • Traditional portfolio assessment relies on
    man-made data collection and a writing-centered
    learning process
  • The web-based learning portfolio can be
    collected, stored, and managed automatically by
    computers when learners interact with an
    e-learning platform

62
Research background (cont.)
  • Data mining
  • Data mining is an appropriate method of knowledge
    discovery to excavate the implicit information
    from large repositories.
  • The interpretable knowledge could be discovered
    by methods of data mining.
  • Grey relational analysis
  • Fuzzy association rule
  • Fuzzy inference

63
Motivation
  • The traditional web-based learning assessment
    only considers final outcomes without considering
    the learning process of learners.
  • Web-based learning portfolio could reveal the
    authentic learning process and be recorded
    automatically by computer.
  • To perform the learning performance assessment
    using web-based learning portfolio or log data is
    becoming a critical issue in the web-based
    learning field.

64
Contribution
  • Teachers could understand the factors that affect
    learning performance in a web-based learning
    environment according the obtained interpretable
    learning performance assessment rules.
  • The proposed learning assessment approach can
    correctly evaluate learners learning performance
    according to their learning portfolios.
  • For Instructor
  • To help teachers to precisely perform the
    formative assessment for individual learner
    utilizing only the learning portfolio in a
    web-based learning environment during learning
    processes.
  • For Learner
  • The evaluation results of learning are applied to
    help teachers immediately examine learning
    progress of learners and perform interactively
    on-line control learning.

65
System architecture of personalized e-learning
system (PELS)
66
User Interface of personalized e-learning system
(PELS)
67
The proposed learning performance assessment agent
  • The gathered learning portfolio on PELS
  • Flowchart of the proposed learning performance
    assessment agent
  • The used methods

68
The gathered learning portfolio on PELS( 1/3 )
69
The gathered learning portfolio on PELS ( 2/3
)-the learning factor RR-
  • The reading rate of course materials is defined
    as the rate of studying course materials in a
    course unit.

A calculating example for the learning factors
of reading rate
70
The gathered learning portfolio on PELS(3/3 )
  • The learning portfolio obtained from the
    three-years students of Taipei County Jee May
    Elementary School.
  • The total number of learning records is 583.
  • Training data 400 learning records
  • Test data 183 learning records

71
The flowchart of the learning performance
assessment agent - Scheme 1
72
The flowchart of the learning performance
assessment agent - Scheme 2
73
The Used Methods
  • Grey relational analysis for learning factor
    analysis
  • Fuzzy association rule mining for learning
  • performance assessment rule discovery
  • Fuzzifying
  • Calculating large itemsets
  • Discovering fuzzy rules
  • Fuzzy inference
  • Fuzzy inferring
  • Defuzzification

74
Grey Relational Analysis
  • Performing learning factor analysis
  • Comparing the grey relational grades between the
    referred sequence Grade(I0 ) and comparative one
    RR,RT.(ii )

Relative learning portfolio
Primitive learning portfolio
75
Fuzzy association rulefuzzifying (1/4)
  • To discover fuzzy rules of various grade levels,
    the learning portfolio are partitioned into 5
    non-overlapped groups including Grade.VH,
    Grade.H, Grade.M, Grade.L, and Grade.VL according
    to the membership degrees mapped to each cluster
    center of final testing score Grade.

76
Fuzzy association rulefuzzifying (2/4)
  • To classify portfolio based on the learning
    factor Grade by the membership function

77
Fuzzy association rulefuzzifying (3/4)
  • The five groups partitioned by the grade level
    for mining the learning performance assessment
    rules and verifying the performance of learning
    assessment

78
Fuzzy association rule fuzzifying (4/4)
  • Those quantitative learning record data of the
    portfolio must be transferred into fuzzy learning
    record data for fuzzy association rule mining.
  • The clustering centers of five learning factors
    can be appropriately determined by K-means
    clustering algorithm, thus determining the
    membership functions of triangle fuzzy sets.

79
Fuzzy association rulelarge itemset
  • The fuzzy rules composed by the large itemset

80
Fuzzy association rulefuzzy rules base (1/2)
  • To ensure that the discovered rules are
    interesting and accurate, three conditions that
    include the minimum support , minimum confidence
    and minimum certainty factor are deliberated

81
Fuzzy association rulefuzzy rules base (2/2)
  • From the discovered fuzzy rules, we find that
  • No rules are discovered in the grade level G.VL
    due to fewer training data

The level of Grade The amount of rules
Grade.VL 0
Grade. L 6
Grade.MID 7
Grade.H 11
Grade.VH 14
The amount of rules in 5 groups
82
Fuzzy inferenceFuzzifying
  • The following table is an example of learning
    portfolio.
  • To infer the grade of the final test for this
    learner according to the learning factors RR, RT,
    LA, CR,and HL
  • The following table presents fuzzy values and
    level of each item transferred by the membership
    function

83
Fuzzy inferenceFuzzy inferring (1/4)
  • The following 11 rules are triggered by the
    learning portfolio of the learner with student ID
    11737

84
Fuzzy inferenceFuzzy inferring (2/4)
-Grade.H


85
Fuzzy inferenceFuzzy inferring (3/4)
-Grade.MID
86
Fuzzy inferenceFuzzy inferring (4/4)
-Grade.L
87
Fuzzy inference defuzzification
  • Real_grade 86.3043
  • Infer_grade 86.3076

88
Fuzzy inference
89
Experiments
  • Accuracy rate of the learning performance
    assessment
  • 5points
  • The score level
  • To consider various combinations of learning
    factors
  • A, B, C, D, ECR-LA-HL-RT-RR, CR-LA-HL-RT,
    CR-LA-HL, CR-LA, CR
  • The infer example of A, C, E

90
Experiments 5 points
  • The method of 5 points is used to evaluate the
    accuracy rate of the predicted learning
    performance. That is, if the difference of the
    predicted test score with the actual test score
    is between -5 points to 5 points, then the
    predicted result is served as correct.

83.33
91
Experiments score level
  • The score level is judged as Grade.M if the
    actual test score of some learner is 85.16
    because the linguistic term of the Grade.M has
    largest mapped membership degree compared to the
    other linguistic terms of the final grade

66.67
92
various combinations of learning factors the
infer example of A, C, E
  • The following chart present the accuracy of the
    infer_grade of 3 itemsets, A?C?E
  • ACR, LA, HL, RT, RA
  • 33.33
  • CCR, LA, HL
  • 83.33
  • E CR
  • 83.33

93
Experiments various combinations of learning
factors
  • The predicted accuracy rates of 400 training data
    and 183 testing data under considering various
    combinations of learning factors for the two
    proposed accuracy evaluation methods
  • The combination of factors are more relative with
    learning performance, the accuracy is better.


94
Experiments accuracy rate of factor CR
  • Comparison results of the predicted accuracy
    rates of various grade levels for 400 training
    data and 183 testing sample under only
    considering the learning factor CR

95
The Other Intelligent Web Systems
  • News Archive and Data Mining Agent System
  • Chinese Word Segmentation System with Intelligent
    New Word Extension

96
New Archive And Data Mining Agent System
97
New Archive And Data Mining Agent System
98
New Archive And Data Mining Agent System
99
Chinese Word Segmentation System with Intelligent
New Word Extension
100
Chinese Word Segmentation System with Intelligent
New Word Extension
101
Chinese Word Segmentation System with Intelligent
New Word Extension
The designed fuzzy rule base for inferring the
confidence degree of new word
102
Q A TimeThanks for your listening!
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