Abalone
Abstract
Predict the age of abalone from physical measurements
Purpose
Additional Information Predicting the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem. From the original data examples with missing values were removed (the majority having the predicted value missing), and the ranges of the continuous values have been scaled for use with an ANN (by dividing by 200).
| Name | Role | Type | Description | Missing |
|---|---|---|---|---|
| Sex | Feature | Categorical | M, F, and I (infant) | No |
| Length | Feature | Real | Longest shell measurement | No |
| Diameter | Feature | Real | perpendicular to length | No |
| Height | Feature | Real | with meat in shell | No |
| Whole Weight | Feature | Real | whole abalone | No |
| Shucked Weight | Feature | Real | weight of meat | No |
| Viscera Weight | Feature | Real | gut weight (after bleeding) | No |
| Shell Weight | Feature | Real | after being dried | No |
| Rings | Target | Integer | +1.5 gives the age in years | No |
Papers Citing this Dataset
56 papers found
Sequential Learning with LS-SVM for Large-Scale Data Sets
By By Tobias Jung, Daniel Polani.
Sparse Gaussian processes using backward elimination
By By Liefeng Bo, Ling Wang, Licheng Jiao.
Kronecker factorization for speeding up kernel machines
By By Gang Wu, Zhihua Zhang, Edward Chang.
Exploration of textual document archives using a fuzzy hierarchical clustering algorithm in the GAMBAL system
By By Vicenç Torra, Sadaaki Miyamoto, Sergi Lanau.
A Comparison of Model Aggregation Methods for Regression
By By Zafer Barutçuoglu.