Use case er ennå ikke i boks. En egendefinert utgave kan bli løsningen. Trenger i tilfellet en brainstorm for å gjøre det bra.

Har skrevet ned ca en siden angående related work, som sammenliger med forrige semesters oppgave. Denne sammenligningen viser egentlig akkurat det som jeg hadde håpet. Men jeg må jobbe litt for å få argumentert for DE metoden. Mange hull der som må etterforskes.

Koden er ganske ok, men som sist ble den ikke stort bedre enn den var. Jeg prøver å følge med om det er noe som kan forbedres underveis.

Oppsummering:

Har litt problemer med å finne noe om Nevrale Nett som kan brukes.

Har fått jobbet veldig lite med koden i dag.

Har kommet litt fremover mtp use case, venter på en som skal forklare hva som skal gjøres.

Tidsbruk i dag er maks 3 timer. De var ikke særlig effektive. Brukt mesteparten på Compendex.

Sitter på IRIS.

RiskE er installert, men det er ingen som har lyst ut sine ideer for meg ennå. Venter på det.

Satser på at related work biten er kommet i gang, vet ikke om det jeg har kan brukes, men det er en veldig omfattende prosess å lære Nevrale Nett.

Skriver en del i Google Docs, kun for meg selv.

Liten fremdrift.

Skal på IRIS for å få adgangskort. Har ikke fått sett på softwaren for Risky ennå.

Gjør et nytt forsøk på å snakke med TE i dag.

Kvantifisere alle verdier.

Related work: MAUT

Rough Set

Vil prøve å endre på Unionen av klasser, fra "or better" til "exact", så kjøre algoritme på den.

Skal også sammenligne med MAUT.

Studerer nevrale nett for å sammenlikne med DRSA/DOMLEM.

Prøver å finne en god kilde som beskriver nevrale nett.

An arti FIcial neural network (ANN), or simply neural network (NN), is a

computational model of how a biological neural network works. To model the

biological neural network a set of neurons are de ned as numerical values.

A set of numeric weights represents the connections between the neurons.

These weights can be adjusted by a training method, such that the neural

network can learn simple concepts.

ArtiFI cial neural networks has been used in several real life applications

like image data processing, tra c control, regression analysis, pattern recognition

and classi cation like hand written character recognition.

Some studies have also tried to utilize neural networks in gene prediction.

According to Reese [18] the FI rst attempt was done by Brunak et al.[8]. Later,

similar methods have been in other works (References [18, 14, 12, 6]).

Scope of Work, Hypothesis line under work

Given the paper written last semester, and given we have the use cases, we can subtract information.

Artificial Neural Networks

NN technology, used for development of artificial intelligence. It is inspired by how the brains of humans

and animals work. The brain is composed of millions of neurons, and these neurons are

connected to each other by axioms and dendrites. The connections are adaptive, and so the

connection structure is dynamically changing. Changes of the connections is what we call

learning.

An artificial neural network is similar, where the strength of couplings between artificial neurons are described by numerical weights. One of the most used structure is the multilayer neural network. The neurons are usually modelled in three layers, where the first layer is called the input layer, the intermediate layer is called the hidden layer, and the last layer is called output layer. The neurons could also be referred to as nodes. The nodes of each layer are connected each other with adjustable weights. The process of adjusting these weights is called training. The numerical value of a unit is the sum of all its input connections times the weight of that input connection. The numerical value of each neuron is the adjusted with a non-linear activation function.

An ANN can be trained to recognize input patterns, give the desired result, by adjusting the weights. A neural network is therefore a general approximation function. A neural network approximation function can have an arbitrary number of inputs and outputs.

Feedforward calculation

After the weights have been trained in the neural network, it can be used to evaluate an input feature vector an predict an associated outcome vector. The process of taking an input vector and predicting an output is called feedforward operation. The feedforward operation is given by equations 2.1, 2.2, 2.3 and 2.4. The input vector is denoted by x.

Backpropagation

Found this, so far good, tutorial: http://www.ai-junkie.com/ann/evolved/nnt4.html

One negative, it is more complicated to construct the network:

Now we have defined our inputs and our outputs what about the hidden layer/s? How do we decide how many layers we should have and how many neurons we should have in each layer? Well, this is a matter of guesswork and something you will develop a ‘feel’ for. There is no known rule of thumb although plenty of researchers have tried to come up with one. By default the simulation uses one hidden layer that contains six neurons although please spend some time experimenting with different numbers to see what effect they may have. I’d like to emphasise here that the more you play around with all the parameters the better the ‘feel’ you are going to develop and the better your neural networks will be.

Møte hos Hein kl 13 for å se på mulighetene for SONDe i oppgaven.

Skal også innom Trygve Eftestøl for å prate med han om nevrale nett.

Fjerne et kriterie = OK dersom ikke inconsistencies.

Dominance-based Rough Set Approach to decision under uncertainty and time preference

Salvatore Greco · Benedetto Matarazzo · Roman Słowin ́ski

Fikk ikke snakket med Trygve Eftestøl, satser på å gjøre det i morgen.

Må begynne med prosessen å sammenligne related works dersom artikkel skal sendes.

Funnet et C# bibliotek, ser på det.

Skal på IRIS i morgen, får forhåpentligvis software da, slik at jeg kan anpasse use caset.

Ble enige på møte med Hein at SONDe paperet ikke brukes i oppgaven nå.

Masteroppgaven er tatt ut, Check!

Fikk vite karakteren på rapporten jeg skrev til jul.

Har funnet ut et eksempel som ikke er klassifiserbart:

The start took: 24919896

Object One Two Three Overall

_________________________________________________

1 1 0 1 0

2 2 1 2 0

3 2 0 1 2

4 0 2 1 0

5 2 1 2 2

6 2 2 1 0

7 2 1 0 0

8 2 2 0 1

9 0 0 1 0

10 2 2 0 0

{ 1 2 4 6 7 9 10 }

{ 1 2 4 6 7 8 9 10 }

{ 3 5 8 }

{ 3 5 }

Approximations

{ 1 4 7 9 }

{ 1 2 3 5 4 6 8 7 9 10 }

Doubtful: { 2 3 5 6 8 10 }

{ 1 2 4 6 7 8 9 10 }

{ 1 2 4 6 7 8 9 10 }

Doubtful: { }

{ 3 5 }

{ 3 5 8 6 10 }

Doubtful: { 8 6 10 }

{ }

{ 3 2 6 5 }

Doubtful: { 3 2 6 5 }

Minimal CERTAIN rules

Skift

Prøver å finne regler

Starten på DOMLEM

Alle objekter igjen:

{ 1 4 7 9 }

e = One <= 0 { 4, 9, } 1 2

e = One <= 1 { 1, 4, 9, } 1 3

e = One <= 2 { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, } 0,4 4

e = Two <= 0 { 1, 3, 9, } 0,67 2

e = Two <= 1 { 1, 2, 3, 5, 7, 9, } 0,5 3

e = Two <= 2 { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, } 0,4 4

e = Three <= 0 { 7, 8, 10, } 0,33 1

e = Three <= 1 { 1, 3, 4, 6, 7, 8, 9, 10, } 0,5 4

The best:

e = One <= 1 { 1, 4, 9, } 1 3

--------- Best størrelse --------- : 1

{ 1 4 9 }

If (One <= 1) then x is atMost bad { 1 4 9 }

Starten på DOMLEM

Alle objekter igjen:

{ 7 }

e = One <= 2 { 2, 3, 5, 6, 7, 8, 10, } 0,14 1

e = Two <= 1 { 2, 3, 5, 7, } 0,25 1

e = Three <= 0 { 7, 8, 10, } 0,33 1

The best:

e = Three <= 0 { 7, 8, 10, } 0,33 1

--------- Best størrelse --------- : 1

{ 7 8 10 }

####### Complex is not subset of Rough Approximation

The new best emementary conditions:

e = Three <= 0 { 7, 8, 10, } 0,33 1

e = Two <= 1 { 7, } 1 1

--------- WHILE Best størrelse --------- : 2

The NEW Complex:

{ 7 }

If (Three <= 0) and (Two <= 1) then x is atMost bad { 7 }

Slutten på DOMLEM

################################### # # # # # # ¤ ¤ ¤ ¤ ¤ ¤ ¤

Skift

Prøver å finne regler

Starten på DOMLEM

Alle objekter igjen:

{ 1 2 4 6 7 8 9 10 }

e = One <= 0 { 4, 9, } 1 2

e = One <= 1 { 1, 4, 9, } 1 3

e = One <= 2 { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, } 0,8 8

e = Two <= 0 { 1, 3, 9, } 0,67 2

e = Two <= 1 { 1, 2, 3, 5, 7, 9, } 0,67 4

e = Two <= 2 { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, } 0,8 8

e = Three <= 0 { 7, 8, 10, } 1 3

e = Three <= 1 { 1, 3, 4, 6, 7, 8, 9, 10, } 0,88 7

e = Three <= 2 { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, } 0,8 8

The best:

e = One <= 1 { 1, 4, 9, } 1 3

--------- Best størrelse --------- : 1

{ 1 4 9 }

If (One <= 1) then x is atMost medium { 1 4 9 }

Starten på DOMLEM

Alle objekter igjen:

{ 2 6 7 8 10 }

e = One <= 2 { 2, 3, 5, 6, 7, 8, 10, } 0,71 5

e = Two <= 1 { 2, 3, 5, 7, } 0,5 2

e = Two <= 2 { 2, 3, 5, 6, 7, 8, 10, } 0,71 5

e = Three <= 0 { 7, 8, 10, } 1 3

e = Three <= 1 { 3, 6, 7, 8, 10, } 0,8 4

e = Three <= 2 { 2, 3, 5, 6, 7, 8, 10, } 0,71 5

The best:

e = Three <= 0 { 7, 8, 10, } 1 3

--------- Best størrelse --------- : 1

{ 7 8 10 }

If (Three <= 0) then x is atMost medium { 7 8 10 }

Starten på DOMLEM

Alle objekter igjen:

{ 2 6 }

e = One <= 2 { 2, 3, 5, 6, } 0,5 2

e = Two <= 1 { 2, 3, 5, } 0,33 1

e = Two <= 2 { 2, 3, 5, 6, } 0,5 2

e = Three <= 1 { 3, 6, } 0,5 1

e = Three <= 2 { 2, 3, 5, 6, } 0,5 2

The best:

e = One <= 2 { 2, 3, 5, 6, } 0,5 2

--------- Best størrelse --------- : 1

{ 2 3 5 6 }

####### Complex is not subset of Rough Approximation

The new best emementary conditions:

e = One <= 2 { 2, 3, 5, 6, } 0,5 2

e = Two <= 2 { 2, 3, 5, 6, } 0,5 2

--------- WHILE Best størrelse --------- : 2

The NEW Complex:

{ 2 3 5 6 }

####### Complex is not subset of Rough Approximation

Hopp i havet

Hopp i havet

The new best emementary conditions:

e = One <= 2 { 2, 3, 5, 6, } 0,5 2

e = Two <= 2 { 2, 3, 5, 6, } 0,5 2

e = Three <= 2 { 2, 3, 5, 6, } 0,5 2

--------- WHILE Best størrelse --------- : 3

The NEW Complex:

{ 2 3 5 6 }

####### Complex is not subset of Rough Approximation

Hopp i havet

Hopp i havet

Hopp i havet

Hopp i havet

The new best emementary conditions:

e = One <= 2 { 2, 3, 5, 6, } 0,5 2

e = Two <= 2 { 2, 3, 5, 6, } 0,5 2

e = Three <= 2 { 2, 3, 5, 6, } 0,5 2

Unhandled Exception: System.NullReferenceException: Object reference not set to an insta

ject.

at FuzzyDecisionSystem.DecisionRuleAlgorithm.PrintElementaryConditions(List`1 list) i

rojects\Visual Studio 2010\SaturdayTest\FuzzyDecisionSystem\DecisionRuleAlgorithm.cs:lin

at FuzzyDecisionSystem.DecisionRuleAlgorithm.FindRules() in D:\Jarle\Projects\Visual

SaturdayTest\FuzzyDecisionSystem\DecisionRuleAlgorithm.cs:line 79

at FuzzyDecisionSystem.DecisionRuleAlgorithm..ctor(RoughApproximation roughApproximat

rix dataMatrix, String ruleType) in D:\Jarle\Projects\Visual Studio 2010\SaturdayTest\Fu

ystem\DecisionRuleAlgorithm.cs:line 31

at FuzzyDecisionSystem.Program.FindRules(RoughApproximation r, DataMatrix dataMatrix)

\Projects\Visual Studio 2010\SaturdayTest\FuzzyDecisionSystem\Program.cs:line 408

at FuzzyDecisionSystem.Program.CreateUpperAndLowerUnions(DataMatrix dataMatrix) in D:

cts\Visual Studio 2010\SaturdayTest\FuzzyDecisionSystem\Program.cs:line 391

at FuzzyDecisionSystem.Program.Main(String[] args) in D:\Jarle\Projects\Visual Studio

ayTest\FuzzyDecisionSystem\Program.cs:line 38

Press any key to continue . . .

jMAF hadde dette å si:

Unclassified

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