adult.names

BP神经网络预测个人的收入信息

| This data was extracted from the census bureau database found at
| http://www.census.gov/ftp/pub/DES/www/welcome.html
| Donor: Ronny Kohavi and Barry Becker,
|        Data Mining and Visualization
|        Silicon Graphics.
|        e-mail: [e-mail] for questions.
| Split into train-test using MLC++ GenCVFiles (2/3, 1/3 random).
| 48842 instances, mix of continuous and discrete    (train=32561, test=16281)
| 45222 if instances with unknown values are removed (train=30162, test=15060)
| Duplicate or conflicting instances : 6
| Class probabilities for adult.all file
| Probability for the label '>50K'  : 23.93% / 24.78% (without unknowns)
| Probability for the label '<=50K' : 76.07% / 75.22% (without unknowns)
|
| Extraction was done by Barry Becker from the 1994 Census database.  A set of
|   reasonably clean records was extracted using the following conditions:
|   ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
|
| Prediction task is to determine whether a person makes over 50K
| a year.
|
| First cited in:
| @inproceedings{kohavi-nbtree,
|    author={Ron Kohavi},
|    title={Scaling Up the Accuracy of Naive-Bayes Classifiers: a
|           Decision-Tree Hybrid},
|    booktitle={Proceedings of the Second International Conference on
|               Knowledge Discovery and Data Mining},
|    year = 1996,
|    pages={to appear}}
|
| Error Accuracy reported as follows, after removal of unknowns from
|    train/test sets):
|    C4.5       : 84.46+-0.30
|    Naive-Bayes: 83.88+-0.30
|    NBTree     : 85.90+-0.28
|
|
| Following algorithms were later run with the following error rates,
|    all after removal of unknowns and using the original train/test split.
|    All these numbers are straight runs using MLC++ with default values.
|
|    Algorithm               Error
| -- ----------------        -----
| 1  C4.5                    15.54
| 2  C4.5-auto               14.46
| 3  C4.5 rules              14.94
| 4  Voted ID3 (0.6)         15.64
| 5  Voted ID3 (0.8)         16.47
| 6  T2                      16.84
| 7  1R                      19.54
| 8  NBTree                  14.10
| 9  CN2                     16.00
| 10 HOODG                   14.82
| 11 FSS Naive Bayes         14.05
| 12 IDTM (Decision table)   14.46
| 13 Naive-Bayes             16.12
| 14 Nearest-neighbor (1)    21.42
| 15 Nearest-neighbor (3)    20.35
| 16 OC1                     15.04
| 17 Pebls                   Crashed.  Unknown why (bounds WERE increased)
|
| Conversion of original data as follows:
| 1. Discretized agrossincome into two ranges with threshold 50,000.
| 2. Convert U.S. to US to avoid periods.
| 3. Convert Unknown to "?"
| 4. Run MLC++ GenCVFiles to generate data,test.
|
| Description of fnlwgt (final weight)
|
| The weights on the CPS files are controlled to independent estimates of the
| civilian noninstitutional population of the US.  These are prepared monthly
| for us by Population Division here at the Census Bureau.  We use 3 sets of
| controls.
|  These are:
|          1.  A single cell estimate of the population 16+ for each state.
|          2.  Controls for Hispanic Origin by age and sex.
|          3.  Controls by Race, age and sex.
|
| We use all three sets of controls in our weighting program and "rake" through
| them 6 times so that by the end we come back to all the controls we used.
|
| The term estimate refers to population totals derived from CPS by creating
| "weighted tallies" of any specified socio-economic characteristics of the
| population.
|
| People with similar demographic characteristics should have
| similar weights.  There is one important caveat to remember
| about this statement.  That is that since the CPS sample is
| actually a collection of 51 state samples, each with its own
| probability of selection, the statement only applies within
| state.


>50K, <=50K.

age: continuous.
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
fnlwgt: continuous.
education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
education-num: continuous.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
sex: Female, Male.
capital-gain: continuous.
capital-loss: continuous.
hours-per-week: continuous.
native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.