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
Data Clustering via Principal Direction Gap Partitioning
By Ralph Abbey, Jeremy Diepenbrock, Amy Langville, Carl Meyer, Shaina Race, Dexin Zhou
Regularization by Intrinsic Plasticity and Its Synergies with Recurrence for Random Projection Methods
By Klaus Neumann, Christian Emmerich, Jochen Steil
Dependence Tree Structure Estimation via Copula
By Jian Ma, Zeng-Qi Sun, Sheng Chen, Hong-Hai Liu
Causal Inference on Time Series using Structural Equation Models
By Jonas Peters, Dominik Janzing, Bernhard Scholkopf
Testing the Gauss linear assumption for on-line predictions
By Valentina Fedorova, Ilia Nouretdinov, Alex Gammerman