“Machine Learning For Decision Making”

Dr.Eng. Luther A. Latumakulita, S.Si., M.Kom

By

Dr.Eng. Luther A. Latumakulita, S.Si., M.Kom

Decision

DecisionMaking

Life is a Choice

Making

OptimalSolution

METHODS

NI vs AI vs ML vs DL

Artificial IntelligenceAI

Machine LearningML

Natural Intelligence (NI)Normally when we thinkof NI we think about howanimal or human brainsfunction, but there is moreto natural intelligencethan neuroscience.

A Technique which

DeepLearning

(Multi-HiddenLayers)

A Technique which enable machine to “Mimic” Human Behaviors

Use statistical methods to enable machines to improve with experience

Human brain process visual and auditory information much faster than modern computer(Neuro-Fuzzy and Soft Computing, Shing Roger Jang)

AI & ML Methods

Fuzzy Logic

Artificial Neural Networks

Decision tree learning

Association rule learning

Support vector machines

Genetic algorithms

Bayesian networks

FocusDiscussion

Fuzzy Logic

TruthValues

Boolean Logic (True/False)

How to handle Uncertain and Vagueness Problems?Vagueness Problems?

Fuzzy Logic(Truth value is a

Degree of Membership)

Fuzzy Logic Architecture

Human Brain Nervous System

Stimulus

Receptors

Response

Neural Net

Effectors

ANN Inspired By Biological Nervous Systems

ANN Architecture

Implementation

Work-1:Propose a Fuzzy Framework for

BM Scholarship Selection Process

9

Process

Published in the Proc. of 2016 IEEE International Conference on Information, Communication Technology and System (ICTS),

Surabaya, Indonesia, pp. 107-113.( DOI: 10.1109/ICTS.2016.7910282

Problem Research

HowLimited Quota

Bidik Misi Scholarship Selection

HowTo Make a Clear

Decision?

Proposed Fuzzy Framework

FLC = Fuzzy Logic Controller

Mamdani Fuzzy Inference System

Design Membership Function

Elbow Methods for P1

Calculate difference of SSE

Linguistic{“Low”, “Average”, “High”}

13

“Elbow”

{“Low”, “Average”, “High”}

13

K-Mean Clustering

Design Membership Function (Cont..)

Design Rule Bases

FLC Economy

Rule #1: IF P1 is Low AND P2 is Few THEN Economy is Poor

Result

Accuracy71.43%

ConfusionMatrix

Cases of Same Manual Rankings

Problem Solved(Clear Decision)

Methods Comparison

Inference Model Accuracy

SugenoOrder 0 56.30%

Order 1 64.71%

Mamdani 71.43%Mamdani 71.43%

E. H. Mamdani, S. Assilian, “An Experiment in Linguistic Synthesis with aFuzzy Logic Controller” International Journal of Man-Machine Studies,Vol. 7, pp.1-13, 1975.

Takagi, T.; Sugeno, M. Fuzzy identification of systems and itsapplications to modeling and control. IEEE Trans. Sys. Man. Cybern.1985, 15, 116–132.

Work-1: Summary

The proposed frameworkbased on Fuzzy Logicapproach can overcome theapproach can overcome theproblem of BM SelectionCommittee in making aCLEAR DECISION with theaccuracy of 71.43%.

Implementation

Work-2:Classification Using ANN

BeforeSelection Using FIS

20

Selection Using FIS

Published in the Proc. of 2017 IEEE 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

(ICNC-FSKD 2017), Guilin, China, pp. 1273-1278.( DOI: 10.1109/FSKD.2017.8392955 )

Work-2: Research Objectives

1 2

Selection Process

Stability of System

Performance

Classification Before Selection

RecommendClass

Priority 1

Selection Process

Classification Process

Non-RecommendClass

Priority 1

Priority 2

Classification Preparation

Assuming quota of 50% from total data Define actual class and target for two

recommendation classes. Design Cross-validation: 80% training Design Cross-validation: 80% training

and 20% testing Design NN-Multilayer Perceptron (NN-

MLP)

504 Data set with incomplete parameters

119 Data set with complete parameters

NN-MLP: Dataset With Complete Parameter

Back-Propagation Training Algorithm

Selection Preparation

Calculate R-values of input parametersfor Redesign the framework and rulebase for each FLC.

Correlation Coefficient (R) of Input Parameters

Redesign Fuzzy Framework

Redesign Rule Bases

Results: Classification and Selection

Complete ParameterClassification Result

Incomplete Parameter

Selection ResultSelection Result

13%71.43%Work-1

2%

System performance remain stable

Successfully Increase Selection Accuracy

Results: Prediction on 121 New Data

Work-2: Summary By considering R-values of input parameters in

redesigning the BM scholarship’s fuzzy framework,the accuracy is INCREASED around 13% (from71.43% to 84.9%).

By implementing the classification process usingANN, the number of candidates which will proceed inANN, the number of candidates which will proceed inthe selection process is reduced, indicated anINCREASING OF SYSTEM EFFICIENCY.

The proposed system provides satisfactory accuracyin both classification and selection processes for twodifferent data set and system PERFORMANCEREMAINS STABLE even there are parameters lostin data set.