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Bayesian Network 을 활용한 예측 분석
1. Bayesian Network 을 활용한 예측 분석 Machine learning 의 관점에서 본 데이터의 활용
최진혁 Ph.D. (인포리언스)
2014. 09.16.
2. Who Am I?
Ph.D. @ KAIST 전산학과
Human-Computer Interaction
Machine Learning / Data Mining
ETRI, KAIST
데이터 기반 홈 미들웨어
Web & SNS Mining (usage, text…)
주식회사 인포리언스 (Inforience Inc.)
Data (Text) Mining
데이터로부터 실제적인 가치를 추출
Collaborative Data Mining System 개발
데이터에 포함된 동적 패턴의 탐색과 해석을 위한 협업적 탐험 플랫폼(Collaborative Data Mining Platform for Searching and Interpretation of Data Dynamics)
http://inforience.net/
3. Who Am I?
"Big data does not need big machines. It needs big intelligence".
그렇다면, Big intelligence 는 어디서부터?
4. 세미나 개념 및 내용
개념
기계학습의 기본 개념 소개 (사례를 통한)
특히, Bayesian Network
비전문가 대상
경험과 의견의 공유
내용
빅 데이터는 충분히 활용되고 있는가?
빅 데이터 활용의 핵심 - Machine Learning
개념과 모델
사례
데이터 기반 추론과 예측
Bayesian Networks
사례
토론
5. 빅 데이터 충분히 활용되고 있나?
빅 데이터가 중요하다는 이야기
모두 지겹다
활용에 관한 구체적인 이야기!!
그러나 충분히 활용되지 못하고 있다는 이야기
6. 빅 데이터 시대의 Machine Learning
증가한 데이터 저장 능력 / 실제로 증가한 데이터
Looks random but certain patterns
어떤 패턴들이 숨어있는지 알 수 없다.
A good or useful approximation
무척 중요하지만…
특별한 분야의 특별한 데이터, 그리고 특별한 해석
데이터 양, 종류, 특성, 활용 분야의 증가
다양한 분야의 다양한 데이터, 그리고 다양한 활용
누구에게나 적용될 수 있는 결과 해석과 활용의 중요성
7. Machine Learning
Inducing general functions from specific training examples
Looking for the hypothesis that best fits the training examples
Inferring a boolean-valued function from training examples of its input and output
9. Machine Learning
Machine Learning을 실제 문제에 적용하는 것은 쉬운가?
General (Ideal) Process vs. Real Process
Machine Learning 으로부터 무엇을 얻어낼 수 있는가?
Inference? Prediction?
Predictive Modeling vs. Explanatory Modeling
10. Machine Learning Examples (1)
Function approximation (Mexican hat)
2 2
3 1 2 1 2 1 2 f (x , x ) sin 2 x x , x , x [1,1]
15. Machine Learning Examples (6)
TV program preference inference based on web usage data
Web page #1 Web page #2 Web page #3 Web page #4 ….
Classifier
TV Program #1 TV Program #2 TV Program #3 TV Program #4 ….
1
2
3
What are we supposed to do at each step?
16. Mining Social Relationship Types in an Organization using Communication Patterns
CSCW 2013
Jinhyuk Choi, Seongkook Heo, Jaehyun Han, Geehyuk Lee, Junehwa Song
Department of Computer Science
KAIST (Korea Advanced Institute of Science and Technology)
17. Objective
Propose a method to…
automatically recognize social relationship types among people in an organization
Using only easily collectable data
indoor co-location data
instant messenger data (rather than e-mail, call logs…)
real-time communication
without having to worry about their conversations being exposed in a shared location
18. Experiment Data collection
Co-location
How long, how often, how regularly
Bluetooth stations at several location points (meeting rooms, Labs, a lounge)
scan the surrounding area at a radius of approximately 10 m, at a 20s frequency
collect the Bluetooth IDs of users’ mobile phones
Instant Messenger Data
From participants’ PCs
Record the names of participants conversed with by participants as well as the time of the conversation at one minute intervals
6th floor at KAIST Computer Science building
Bluetooth Stations
19. Experiment Data collection
Participants
22 computer science graduate students
Belong to several different concentrations.
Same concentration
close seats & regular meetings in the meeting room
For one month
User survey (question about 21 other participants)
20. Experiment Data analysis
: detected time slot
: non- detected time slot
User #3
User #1
User #2
TIME
푡12 푘: 14
푡13 푘: 5
Location : k
푓12 푘: 2
푓13 푘: 1
푡1 푘: 24
푡2 푘: 17
푡3 푘: 11
푓1 푘: 4
푓2 푘: 3
푓3 푘: 2
21. Experiment Data analysis
co-visit-duration (no. of detected time slot)
how long a particular user i stays with another user j at a particular location k
co-visit-frequency (no. of detected groups)
how often a particular user i visited a location k with another user j
co-visit-average-duration
co-visit-hour-regularity
co-visit-weekday-regularity
mess-comm-duration
mess-comm number
mess-comm-ave-time
From IM…
Total 18 indicators !!
22. co-visit-frequency
(Meeting room)
2 4 6 8 10 12 14 16 18
0
0.05
0.1
0.15
HIR Classification
IG
2 4 6 8 10 12 14 16 18
0
0.05
0.1
HFR Classification
IG
Experiment
Data analysis
Lounge Lab Messenger Meeting
room
co-visit-duration
(Lounge)
HIR Classification: HIR or not
HFR Classification: HFR or not
Indicator numbers
Indicator numbers
24. To build accurate user profile
Navigational page elimination – “not fully
explored”
Using no. of contained hyperlinks or URL lengths
(Cooley, Mobasher, & Srivastava, 1999; Domenech &
Lorenzo, 2007)
Manually
(Kelly & Belkin, 2004)
High-Interested Contents Page Retrieval
Figure from “Data Preparation for Mining World Wide
Web Browsing Patterns”, Journal of Knowledge and
Information Systems, 1999
1 2
0
2000
4000
6000
8000
10000
12000
14000
16000
No. of Web pages
1-Navigational/2-Contents
1 2 3 4 5
0
1000
2000
3000
4000
5000
6000
No. of Web pages
Interest Levels
Hypothesis
Users will visit navigational pages more frequently
& regularly
Users will show more interactions at interested
pages
High interested page identification
Interaction logs (many references)
Visit frequency & revisit patterns
(Adar, Teevan, & Dumais, 2008; Aula, Jhaveri, &
Kaki, 2005)
25. c c c c c c c c c c c c c c c c c c c c c c c c c
Day frequency (DF)
Visit number in a day (VnD)
Interaction logs (day mean)
Session frequency (SF)
Visit number in a session (VnS)
Interaction logs (session mean)
|{ : }|
| |
j i j
i
d Url d
DF
D
ij
ij
kj
k
n
VnD
n
|{ : }|
| |
j i j
i
s Url s
SF
S
ij
ij
kj
k
m
VnS
m
Total 16 features
High-Interested Contents Page Retrieval
27. Day frequency
Session frequency
Visit number in a day
Usage data (day mean)
Visit number in a session
Usage data (session mean)
N-day buffer
1-day buffer
1-day buffer
1-Session buffer
The first Classifier
The second Classifier
High valued
Web Pages
Navigational
pages
Low-interested pages
: data calculation modules : Sessions : Web pages
Contents pages
High-Interested Contents Page Retrieval
29. Bayesian Networks Introduction
Graphical models, probabilistic networks
causality and influence
Nodes are hypotheses (random vars) and the prob corresponds to our belief in the truth of the hypothesis
Arcs are direct influences between hypotheses
The structure is represented as a directed acyclic graph (DAG)
Representation of the dependencies among random variables
The parameters are the conditional probs in the arcs
움직임
소리
진동
밝기
수행 기능
30. Bayesian Networks Introduction
Learning
Inducing a graph
From prior knowledge
From structure learning
Estimating parameters
Inference
Beliefs from evidences
Especially among the nodes not directly connected
?????
31. Structure Introduction
Initial configuration of BN
Root nodes
Prior probabilities
Non-root nodes
Conditional probabilities given all possible combinations of direct predecessors
A
B
D
E
C
P(b)
P(a)
P(d|ab), P(d|aㄱb), P(d|ㄱab), P(d|ㄱaㄱb)
P(e|d) P(e|ㄱd)
P(c|a)
P(c|ㄱa)
32. Causes and Bayes’ Rule Introduction
Diagnostic inference:
Knowing that the grass is wet,
what is the probability that rain is
the cause?
causal
diagnostic
33. Causal vs Diagnostic Inference Introduction
Causal inference: If the sprinkler is on, what is the probability that the grass is wet? P(W|S) = P(W|R,S) P(R|S) + P(W|~R,S) P(~R|S) = P(W|R,S) P(R) + P(W|~R,S) P(~R) = 0.95*0.4 + 0.9*0.6 = 0.92
34. Bayesian Networks: Causes Introduction
Causal inference:
P(W|C) = P(W|R,S) P(R,S|C) +
P(W|~R,S) P(~R,S|C) +
P(W|R,~S) P(R,~S|C) +
P(W|~R,~S) P(~R,~S|C)
and use the fact that
P(R,S|C) = P(R|C) P(S|C)
Diagnostic: P(C|W ) = ?
35. Bayesian Networks: Inference Introduction
P (C,S,R,W,F ) = P (C ) P (S |C ) P (R |C ) P (W |R,S ) P (F |R )
P (C,F ) = ΣS ΣR ΣW P (C,S,R,W,F )
P (F |C) = P (C,F ) / P(C ) Not efficient!
Belief propagation (Pearl, 1988)
Junction trees (Lauritzen and Spiegelhalter, 1988)
Independence assumption
36. Inference Evidence & Belief Propagation
Evidence – values of observed nodes
V3 = T, V6 = 3
Our belief in what the value of Vi ‘should’ be changes.
This belief is propagated
V1
V5
V2
V4
V3
V6
37. Belief Propagation
V
U2
V1
V2
U1
π(U2)
π(V1)
π(V2)
π(U1)
λ(U1)
λ(V2)
λ(V1)
λ(U2)
38. Evidence & Belief
V1
V5
V2
V4
V3
V6
Evidence
Belief
Evidence
Works for classification ??
39. Applying Bayesian Network
데이터 수집
현재 상황 데이터 (Evidence!!!)
-매우 불완전 일부 변수만 확인 가능
추론
추론 모델 구축
추론 모델
A
B
C
D
E
F
G
A
C
G
F
B
A
C
G
F
B
Exploratory study 가 필요!!!
Data Preprocessing & Cleaning
40. Examples
Modeling Vehicle Choice and Simulating Market Share with Bayesian Networks
Identifying Priorities for Maximizing Repurchase Intent
Vehicle Size, Weight, and Injury Risk
Knowledge Discovery in the Stock Market
41. APPLICATION OF BAYESIAN NETWORKS TO ANALYZE IN ANALYZING INCIDENTS AND DECISION-MAKING TRB 2005 Annual Meeting
This study uses BNs as a knowledge discovery process to accurately predict incident
1. Ctimetotal = Total Clearance Time
2. Typeincide = Type of Incident
3. Policeveh = Number of Police Vehicles
4. Ambulances = Number of Ambulances
5. Fireengines = Number of Fire Engines
6. NbrofInjur = Number of Injuries
7. Nbrtrtrliv = Number of Trucks Involved
8. Nbrcarsinv = Number of Cars Involved
9. Totalanes = Total Number of Lanes
10. Freeway = Type of the Roadwayt clearance time
41
42. Using Bayesian Networks to Model Accident Causation in the UK Railway Industry Probabilistic Safety Assessment and Management 2004, pp 3597-3602
SPAD (Signals Passed at Danger)
Organisational factors
Events attributed to human error and blamed on an operator have systemic causes, such as procedural or organisational weaknesses.
Modelling the Organisational Context
43. 해상 사고 데이터 분석 과정
공공 데이터 포털에서 2007~2013년 사이의 해상 사고 데이터 (엑셀 형식) 를 다운로드
원 데이터 형태는 아래 그림과 같음 (각 연도별로 탭을 만들어 저장되어 있는 형태)
45. 해상 사고 데이터 분석 과정
•예1) 그림의 노드 6 (사고 유형)을 CD(충돌)로 설정할 경우 (실제로 충돌유형의 사고가 보고되었다는 가정)
•노드 5 (사고해역) 의 확률분포는 변화가 없음
•CAUSE 노드는 WH (운항부주의) 값이 현저하게 상승
•충돌사고는 운항부주의가 원인이라고 추론할 여지가 있음
46. 해상 사고 데이터 분석 과정
예2) 그림의 노드 6 (사고 유형)을 HJ(화재)로 설정할 경우, (실제로 화재 유형의 사고가 보고되었다는 가정)
•CAUSE 노드에서는 기타 원인 (ETC)와 화기취급부주의 (HG) 의 확률 값이 크게 상승
•노드 5 (사고해역) 에서는 항계내(HGN) 의 확률값이 현저히 상승함
•(화재 사고는 항계내 해역에서 많이 발생하며 화기취급부주의가 가장 큰 원인이 된다고 해석 가능함)
50. Analytic Modeling
Bayesian networks can be built from human knowledge, i.e. from theory, or, they can be machine learned from data.
Bayesian networks allow human learning and machine learning to interact efficiently.
Bayesian network models can cover the entire range from association to causation
Predictive modeling as well as explanatory modeling
52. Big machine, data analysis, Inference Algorithms, but NOT enough
무엇이 더 필요한가?
53. Discussion
Inference Algorithms, but NOT enough
More Required
Exploration & Interpretation
경험, 도메인 지식의 적용
Domain Experts & Mining Experts
협업의 필요성
Collaborative
해석 결과 공유
해석 결과 공유
해석 결과 공유
해석 결과 공유
시각화 해석
시각화
해석
해석
해석
시각화
시각화
DATA DATA
DATA
DATA
54. References
Textbooks
Ethem ALPAYDIN, Introduction to Machine Learning, The MIT Press, 2004
Tom Mitchell, Machine Learning, McGraw Hill, 1997
Neapolitan, R.E., Learning Bayesian Networks, Prentice Hall, 2003
Jiawei Han, Micheline Kamber, and Jian Pei, Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011.
Materials
Serafín Moral, Learning Bayesian Networks, University of Granada, Spain
Zheng Rong Yang, Connectionism, Exeter University
KyuTae Cho ,Jeong Ki Yoo ,HeeJin Lee, Uncertainty in AI, Probabilistic reasoning, Especially for Bayesian Networks
Gary Bradski, Sebastian Thrun, Bayesian Networks in Computer Vision, Stanford University
Websites
http://library.bayesia.com/display/whitepapers/White+Papers
https://www.facebook.com/dan.ariely/posts/904383595868
http://tomfishburne.com/2014/01/big-data.html
http://news.dice.com/2012/07/17/businesses-struggling-with-data-flood-survey/
http://www.slideshare.net/jeric14/201305-hadoop-jplv3
Papers
Daniel Siewiorek et. al. "SenSay: A Context-Aware Mobile Phone", Proceeding ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
A. Krause et. al, “Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing”, ISWC 2005
