More on Business Intelligence and Other Things

Quadrant Neighborhood

Mann-Whitney reports the following regarding the distributions in the chart below.

Rank Dataset

0 albrecht

0 china

0 coc81

1 cocomo_sdr

1 desharnais_1_1

1 finnish

1 kemerer

1 maxwell

1 nasa93

More on the Bussman9

Nomograms have been implemented for Summarization (and arguably Goals & Benchmarking). The nomogram accepts target classes such as "<> 20000" as an example of bad effort. This allows us to show two parts of the space... The nomogram also indicates where "you are here" lies on the scale.

Overlays

Modeling/Simulation

Trends (needs to be splined)

Alerts

Forecasting

Are there favourite subsets?

Noise injection experiments showed that it is difficult to introduce noise to effort datasets. Then this raised the question: Are certain subsets of the datasets more preferable than the others? To answer that we can look at neighbor-ordering matrices. Two forms of neighbor-ordering are:
* Absolute : here.
* Percentage : here.

Business Intelligence & Quadrant Differences Graphed

All of the above can be found HERE.

Results from Noise Generation in Effort Sets

Ran kNN and kNN with Cliff instance selection for k = 1,3,5.

Noise Injection method:

for 10%, 20%,...,80% of train data:
Replace with a randomly selected effort score is not equal to the original value.
Perform similar transform with the top 3 attributes as selected by BORE also ensuring replaced value is not equal to the original value.

Results are MDMRE from 5x5 cross-validation.

albrecht
china
cocomo
desharnais
finnish
isbsg
maxwell
miyazaki94
nasa93

Results from Noise Reduction in Effort Sets

http://www.unbox.org/wisp/var/kel/cliff-effort/results/graph/

Six sets:
albrecht
china
desharnais
maxwell
miyazaki94
nasa93

Business Intelligence

Initial effort results and some images HERE and my first take on visual model is HERE.

ESEM Paper Initial Version

A very basic initial version is here..

Initial BAMBOO Results

http://unbox.org/wisp/var/bill/comba/reports/february/feb8.pdf

Initial results from running BAMBOO are provided in the above file.

Results from Active Learning Projects This Week

Comparison between NB, NB+CLiff, and 1NN+Cliff:
http://www.unbox.org/wisp/var/kel/doc/ActiveLearning/CliffResults/1-31/

Comparison between NB treatments:
http://www.unbox.org/wisp/var/kel/doc/ActiveLearning/CliffResults/1-31-2/

Secret mixture?

Is there a secret percentage mixture when selecting instances from your own company and from a cross-company? Find out here.