Molinas. June 15, 2018
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- Aleesha Ryan
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1 ITT8 SAMBa Presentation June 15, 2018 ling
2 Data The data we have include: Approx 30,000 questionnaire responses each with 234 questions during A data set of 60 questions asked to 500,000 households from Images of 20,000 houses ling
3 Objectives We had two key goals: of houses into poor or not poor Apply a more sophisticated for income ling
4 Given 20,000 of houses, can we apply image recognition to detect houses belonging to poor people? For example: Poor Not Poor ling
5 Issue: The data was unlabelled... To get around this we could: Use the ground truth information in the data to assign labels (difficult) Manually assign labels based on how they look (easy but labourious) The first wasn t feasible so we went with the latter... Labelled data: 672 labelled (all rural from the same region) 80% (537) used for training 20% (135) for testing ling
6 Method Train a convolutional neural network ling Pattern recognition and machine learning - Bishop 2006
7 After training our neural network the classification accuracy on the test set was: Labelled Poor Labelled Not Poor Predicted Poor Predicted Not Poor Total accuracy: 73%. ling
8 Example Classifications Labelled Poor Labelled Not Poor Predicted Poor Predicted Not Poor ling
9 Accuracy Plot of test set accuracy against Epochs ling
10 Urban? We considered trying urban areas but the variation between residences would cause issues. ling More consistent photographs would be required.
11 Previously poverty has been estimated with 60 variables using a linear. So given our data sets is there anyway to improve the or learn from the data. We fit a linear to a selection of data, and perform AIC and BIC to not over-fit the data and achieve some core predictive variables. We also consider applying a nonlinear. ling
12 We looked at the sample data set with 30,000 people and 250 variables obtained by a questionnaire. We removed variables where more than 25% of data was missing, and then rows where data responses were missing. ling
13 This left us with 36 variables. These were mostly categorical, such as University (Yes or No?), Rural or Urban, but also 3 age categories. Then the data was then aggregated into 8,000 households. ling
14 We fitted a generalized linear to the data, attempting to predict the income per household from the other variables. Even with the removal of variables using AIC selection and BIC, the adjusted R-Squared value of , with a mean squared error Extreme Poverty: 81% Poverty: 71% ling
15 ling Due to the categorical nature of the data we attempted a fit a decision tree. We find that the adjusted R-squared value is 0.402, but when a Complexity Parameter (cp=0.01) is added we find that the adjusted R-squared value is Extreme Poverty: 77% Poverty: 71% ling
16 : May never be an appropriate method for Urban houses. However an approach could be to separate types of houses, i.e flat, terrace, room. Housing alone is not a true indicator of income. No knowledge of number of residents when using image recognition. Location is not taken into account. We performed this analysis with our own judgment of houses. Using actual income might improve it, but may also be more inaccurate. Variable size (33). ling
17 : Set-up a method of taking pictures (for example what angle, no people or cars) which might mean image recognition improves in the future. Use satellite of rooves in future. Improve image recognition with geographical locations. Perform more analysis on the questionnaire data, with more than 25% of the data. Use image recognition as a variable in statistical. ling
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