abalone dataset classification
R code for creating simple decision tree classification model with UCI Machine Learning Repository 'Abalone' Dataset. This dataset helps you predict the age of this mollusk. ( Log Out / INFS5100_Abalone_Dataset. Running the perceptron algorithm on the Abalone dataset gave me a 54.9% test accuracy. Details are in my SVM implementation notes. Special care will therefore have to be taken for class assignment.
how to do feature selection and classification on abalone dataset using methods oter than LDA,QDA,PCA AND SEQUENTIAL FEATURE SELECTION.plz provide the suitable code for it. Data set treated as a 3-category classification problem (grouping ring classes 1-8, 9 and 10, and 11 on). Predicting the age of abalone from physical measurements. Learn more. A soft-margin linear SVM using one-vs-one classification also performed pretty well. Figure 1. Considering that the data doesn’t have a fully separating hyperplane (and in fact has a lot of overlap), I’m surprised that the perceptrons performance wasn’t way worse. Datasets. Looking at some of the features’ histograms, it does appear than there is considerable overlap in the classes, especially in the second two classes (red and green). Because of the weird regression-classification entanglement, the multi-classifier will have to take into account the linear arrangement of the 3 classes.
It turns out there’s a lot of overlap amongst the classes, thereby making classification inherently limited. The datasets come from the UCI Machine Learning Repository and are relatively clean by machine learning standards.
The first 75% of samples (3133) form the training set and the remaining (1044) form the testing set.
Curse of dimensionality: kNN suffers from the problem of sparseness when too many features/axes are in play. Use Git or checkout with SVN using the web URL. We want to predict the age using different physical measurements which is easier to measure. Change ), You are commenting using your Google account. Although, we should note that pure guessing would give us a 33% test accuracy, so a ~60% accuracy isn’t all that much to get excited about. Plotting the model’s training and test set average likelihoods vs number of iterations run, I see a good improvement in training (blue) and test (red) accuracy: I implemented the straightforward k-nearest neighbor algorithm to try on the Abalone dataset, and the test accuracy I got was just around 64-66% which seems to reflect the amount of overlap in the data. download the GitHub extension for Visual Studio, Click here for UCI Machine Learning Repository Abalone Dataset page. I implemented the Support Vector Machine algorithm with the help of CVXOPT (a QP problem solver) and also implemented per-class cross-validation to identify good model parameters to use for the Abalone dataset. The data was partitioned into 3 roughly equally sized classes for the classification task: (1) Ages 1-8, (2) ages 9-10, (3) 11-29. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Select Accept all to consent to this use, Reject all to decline this use, or More info to control your cookie preferences. An abalone is an edible mollusk of warm seas that has a shallow ear-shaped shell lined with mother-of-pearl and pierced with respiratory holes. There was no clear value of k to use either, since it depended a lot on the portion of the data I used for training. Imbalanced classes are a common problem in machine learning classification where there are a disproportionate ratio of observations in each class. To learn more about our use of cookies see our Privacy Statement. This collected dataset allows us to attempt to predict the age (rings) of the Abalone without actually counting the rings. Content moved to https://www.informationdensity.net/2018/02/28/dataset-abalone-age-prediction/.
Code is mainly for inspection, visualisation and pre-processing. Other measurements, which are easier to obtain, are used to predict the age. Learn more. We use cookies and similar technologies ("cookies") to provide and secure our websites, as well as to analyze the usage of our websites, in order to offer you a great user experience. Title of Database: Abalone data 2. Change ), You are commenting using your Facebook account. Work fast with our official CLI.
Cross validation determined ideal set of parameters (on the validation set), which gave me an overall accuracy (on the test set) of 67.4% which is the highest I’ve obtained so far on the Abalone dataset. With the Naive Gaussian Bayes classifier, I got a test accuracy of 58.7% which is predictably worse than the full Gaussian classifier above, but not much worse. ( Log Out / they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. This classification model for this dataset will try to learn 3 classes, not merely a 2 class base-case as I’ve handled in earlier datasets.
abalone_age_classification.
Sources: ... (ACNN'96). We use cookies and similar technologies ("cookies") to provide and secure our websites, as well as to analyze the usage of our websites, in order to offer you a great user experience. If nothing happens, download Xcode and try again. 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.
I will describe the results with each. Classification model developed in SAS Enterprise Miner. The age of abalone is ( number of rings +1.5) years. Xoogler exploring Machine learning.
I implemented the gradient descent Logistic Regression classifier (for multiple classes) with Regularization, and was able to get a 64.7% test accuracy, which is the best of the lot I’ve attempted so far. Change ), https://www.informationdensity.net/2018/02/28/dataset-abalone-age-prediction/. length, diameter, shell weights, etc.) I ran cross-validation across lambda: … and picking the good lambda values gave me an overall test accuracy of 65.9%. Using measurements of abalones to predict the age of such abalone, done in various methods. For my second dataset in this series, I picked another classification dataset, the Abalone dataset.
The dataset contains a set of measurements of abalone, a type of sea snail. The Abalone is a type of marine snail animal.
Code is mainly for … they're used to log you in. R code for creating classification model with UCI Machine Learning Repository 'Abalone' Dataset. sex = Male/Female/Infant) and this needs special treatment. To learn more about our use of cookies see our Privacy Statement. The key is to use a number of different measurements (ex. ####Click here for UCI Machine Learning Repository Abalone Dataset page####. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account. For my second dataset in this series, I picked another classification dataset, the Abalone dataset. The hard-margin linear SVM classifier predictably gave very poor results (despite using one-vs-one multi-class classification) because of the overlap between the classes. Features measured include length, width and weight of the abalone as well as its sex. The soft-margin RBF-kernelized SVM classifier gave much better results. This dataset consists of 4177 samples with an age distribution as shown here. I. Blacklip Abalone (_H.
Abalone Predict age of abalone from physical measurements. The Abalone dataset .
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). However, there are some interesting peculiarities to this dataset compared to other simpler classification datasets: I ran this dataset through my earlier algorithms – Bayes Plug-in, Naive Bayes, Perceptron – and finally also implemented the gradient Logistic Regression algorithm as well as the Support Machine Vector algorithm. Although, picking good parameters from the validation results was a little less obvious. If nothing happens, download the GitHub extension for Visual Studio and try again. and dividing the data set into a training set and a testing set on the same lines as other studies with this data set [4,5]. Instead, all the training data points are taken into accounted, but weighted by proximity to the test data point.
You signed in with another tab or window. The age of an Abalone can be found by counting the number of rings in its shell using a microscope, which is a laborious task. ( Log Out /
With the Gaussian Bayes classifier, the test accuracy obtained is around 61.2% which is not too much worse than the other classifiers I tried later (nor compared to the results reported by the original investigators of the dataset.)
However, the original investigators attempted a classification task on this dataset, so that is what I will do as well. The information is a replica of the notes for the abalone dataset from the UCI repository. 48 (ISSN 1034-3288), Marine Resources Division Marine Research Laboratories - Taroona Department of Primary Industry and Fisheries, Tasmania GPO Box 619F, Hobart, Tasmania 7001, Australia (contact: Warwick Nash +61 02 277277, wnash '@' dpi.tas.gov.au), Sam Waugh (Sam.Waugh '@' cs.utas.edu.au) Department of Computer Science, University of Tasmania GPO Box 252C, Hobart, Tasmania 7001, Australia, http://mldata.io/get-data/dataset/label-encoded/abalone, http://mldata.io/get-data/dataset/original-data/abalone, Predictor: continuous from 1-29 except 28. They are split into two categories, classification and regression, based on the type of the field we are trying to predict.
You can always update your selection by clicking Cookie Preferences at the bottom of the page. Data comes from an original (non-machine-learning) study: Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994) "The Population Biology of Abalone (_Haliotis_ species) in Tasmania. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem. rubra_) from the North Coast and Islands of Bass Strait", Sea Fisheries Division, Technical Report No. Class imbalance can be found in many different areas including medical diagnosis, spam filtering, and fraud detection. One of the input columns is categorical (i.e. In this project, I tried using different methods (some from sklearn libraries) to perform the prediction. I found that values of k around 20-25 seemed slightly better performing than others. Histogram of the Abalone data set 3. I set aside 25% of this dataset for test, and trained on the remaining 75%. R code for creating simple decision tree classification model with UCI Machine Learning Repository 'Abalone' Dataset. A soft-margin RBF-kernelized SVM using one-vs-one classification performed nearly as well as the equivalent one-vs-all classification, with a test-accuracy of 66.9%. Feature selection could really help here.
This dataset helps you predict the age of this mollusk.
Soft k-NN: is a version of k_NN in which the “k” is not a fixed boundary. If nothing happens, download GitHub Desktop and try again. View all posts by Erwin. Dataset “Abalone shell” (by Nicki Dugan Pogue, CC BY-SA 2.0) The nominal task for this dataset is to predict the age from the other measurements, so separate the features and labels for training: But first, a closer look at the data. This dataset should ideally be treated as a regression task, since it attempts to predict the age of the Abalone. 1. Change ), You are commenting using your Twitter account. An abalone is an edible mollusk of warm seas that has a shallow ear-shaped shell lined with mother-of-pearl and pierced with respiratory holes. ( Log Out /
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