Arff dataset for classification

Witten and Eibe Frank the new additions are string attributes, date attributes, and sparse instances. This explanation was cobbled together by Gordon Paynter gordon. It has been edited by Richard Kirkby rkirkby at cs. ARFF files have two distinct sections. The first section is the Header information, which is followed the Data information. The Header of the ARFF file contains the name of the relation, a list of the attributes the columns in the dataand their types.

An example header on the standard IRIS dataset looks like this:. The relation name is defined as the first line in the ARFF file. The string must be quoted if the name includes spaces. Attribute declarations take the form of an orderd sequence of attribute statements. Each attribute in the data set has its own attribute statement which uniquely defines the name of that attribute and it's data type.

The order the attributes are declared indicates the column position in the data section of the file. For example, if an attribute is the third one declared then Weka expects that all that attributes values will be found in the third comma delimited column.

If spaces are to be included in the name then the entire name must be quoted. The keywords numericstring and date are case insensitive. String attributes allow us to create attributes containing arbitrary textual values. This is very useful in text-mining applications, as we can create datasets with string attributes, then write Weka Filters to manipulate strings like StringToWordVectorFilter. The data declaration is a single line denoting the start of the data segment in the file.

The format is: data. Each instance is represented on a single line, with carriage returns denoting the end of the instance. Attribute values for each instance are delimited by commas. They must appear in the order that they were declared in the header section i. Missing values are represented by a single question mark, as in: data 4. Dates must be specified in the data section using the string representation specified in the attribute declaration.

Sparse ARFF files have the same header i. Note that the omitted values in a sparse instance are 0they are not "missing" values! If a value is unknown, you must explicitly represent it with a question mark? Warning : There is a known problem saving SparseInstance objects from datasets that have string attributes. In Weka, string and nominal data values are stored as numbers; these numbers act as indexes into an array of possible attribute values this is very efficient.

However, the first string value is assigned index 0: this means that, internally, this value is stored as a 0. When a SparseInstance is written, string instances with internal value 0 are not output, so their string value is lost and when the arff file is read again, the default value 0 is the index of a different string value, so the attribute value appears to change.

To get around this problem, add a dummy string value at index 0 that is never used whenever you declare string attributes that are likely to be used in SparseInstance objects and saved as Sparse ARFF files.Classification Regression Clustering 92 Other Categorical 38 Numerical Mixed Less than 10 10 to Greater than Less than 27 to Greater than Matrix Non-Matrix Data Types.

Default Task. Attribute Types. Anonymous Microsoft Web Data. Audiology Standardized. Breast Cancer Wisconsin Original. Breast Cancer Wisconsin Prognostic. Breast Cancer Wisconsin Diagnostic. Chess King-Rook vs. Contraceptive Method Choice. Molecular Biology Promoter Gene Sequences. Molecular Biology Protein Secondary Structure.

Molecular Biology Splice-junction Gene Sequences. Page Blocks Classification. Optical Recognition of Handwritten Digits. Pen-Based Recognition of Handwritten Digits. Qualitative Structure Activity Relationships.

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Low Resolution Spectrometer. Teaching Assistant Evaluation. Congressional Voting Records. Waveform Database Generator Version 1.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. How can use an. I really recommend liac-arff. It doesn't load directly to numpy, but the conversion is simple:. I found that scipy has a loader for arff files to load them as numpy record arrays. Follow renatopp's answer: assume your data is the iris dataset, there should be 5 dimensional with last one is the class label column.

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arff dataset for classification

Viewed 18k times. Matthew Spencer 2, 1 1 gold badge 19 19 silver badges 26 26 bronze badges. Active Oldest Votes. Thanks for the feedback. Any idea of how can i use this conversation to classify?.

arff dataset for classification

I think the reason is the presence of "relational" attributes in my arff file. Has anyone a solution? Thank you. Do you think i'll need to parse those numpy arrays? What kind of preprocess would i need to do in order to feed some classification algorithm in scikit-learn?

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10 Open-Source Datasets For Text Classification

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Linked Related Hot Network Questions.In the following one can find some information of how to use Weka for text categorization. One can transform the text files with the following tools into ARFF format depending on the version of Weka you are using :.

arff dataset for classification

Most classifiers in Weka cannot handle String attributes. For these learning schemes one has to process the data with appropriate filters, e. The StringToWordVector filter places the class attribute of the generated output data at the beginning. In case you'd to like to have it as last attribute again, you can use the Reorder filter with the following setup:.

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And with the MultiFilter you can also apply both filters in one go, instead of subsequently. Makes it easier in the Explorer for instance. The StringToWordVector filter can also work with a different stopword list than the built-in one based on the Rainbow system. One can use the -stopwords option to load the external stopwords file.

The format for such a stopword file is one stopword per line, lines starting with ' ' are interpreted as comments and ignored. Note: There was a bug in Weka 3. Java was designed to display UTF-8which should include arabic characters. By default, Java uses code page under Windows, which garbles the display of other characters. In order to fix this, you will have to modify the java command-line with which you start up Weka:. The -Dfile. If you are starting Weka via start menu and you use a recent version at least 3.

Weka Wiki. The above directory structure can be turned into an ARFF file like this: java weka. CSVLoader file. Third-party tools TagHelper Toolswhich allows one to transform texts into vectors of stemmed or unstemmed unigrams, bigrams, part-of-speech bigrams, and some user defined features, and then saves this representation to ARFF.

Currently processes English, German, and Chinese. Spanish and Portugese are in progress. Working with textual data Conversion Most classifiers in Weka cannot handle String attributes.

arff dataset for classification

In case you'd to like to have it as last attribute again, you can use the Reorder filter with the following setup: weka. Reorder -R 2-last,first And with the MultiFilter you can also apply both filters in one go, instead of subsequently.

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Stopwords The StringToWordVector filter can also work with a different stopword list than the built-in one based on the Rainbow system. In order to fix this, you will have to modify the java command-line with which you start up Weka: java -Dfile.Abstract : Classification of urban land cover using high resolution aerial imagery. Intended to assist sustainable urban planning efforts.

Contains training and testing data for classifying a high resolution aerial image into 9 types of urban land cover. Multi-scale spectral, size, shape, and texture information are used for classification. There are a low number of training samples for each class and a high number of classification variablesso it may be an interesting data set for testing feature selection methods. The testing data set is from a random sampling of the image. Class is the target classification variable.

The land cover classes are: trees, grass, soil, concrete, asphalt, buildings, cars, pools, shadows. Johnson, B. Classifying a high resolution image of an urban area using super-object information. High resolution urban land cover classification using a competitive multi-scale object-based approach.

Remote Sensing Letters, 4 2 Please cite: 1. Center for Machine Learning and Intelligent Systems.One of the popular fields of research, text classification is the method of analysing textual data to gain meaningful information.

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Text classification can be used in a number of applications such as automating CRM tasks, improving web browsing, e-commerce, among others. In this article, we list down 10 open-source datasets, which can be used for text classification.

The Amazon Review dataset consists of a few million Amazon customer reviews input text and star ratings output labels for learning how to train fastText for sentiment analysis.

The size of the dataset is MB. Get the data here. The Enron Email Dataset contains email data from about users who are mostly senior management of Enron organisation. This dataset contains reviews from the Goodreads book review website along with a variety of attributes describing the items.

It includes reviews, read, review actions, book attributes and other such. There are a total number of items including 1, This is a dataset for binary sentiment classification, which includes a set of 25, highly polar movie reviews for training and 25, for testing.

This dataset is a collection of movies, its ratings, tag applications and the users. There are two sets of this data, which has been collected over a period of time. The small set includesratings and 3, tag applications applied to 9, movies by users, and the large set includes 27, ratings and 1, tag applications applied to 58, movies byusers.

The large set also includes tag genome data with 14 million relevance scores across 1, tags.

Attribute-Relation File Format (ARFF)

This data set contains full reviews for cars and hotels collected from Tripadvisor and Edmunds. The dataset contains full reviews of hotels in 10 different cities as well as full reviews of cars for model-yearsand In the dataset, the total number of car reviews include approximately 42, and the total number of hotel reviews include approximatelyThe dataset has one collection composed by 5, English, real and non-encoded messages, tagged according to being legitimate or spam.

The dataset is available in both plain text and ARFF format. The Blog Authorship Corpus consists of the collected posts of 19, bloggers gathered from blogger. The corpus incorporates a total ofposts and over million words or approximately 35 posts and words per person. WordNet is a large lexical database of English where nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms synsets and each expressing a distinct concept.

In this dataset, the total number of synsets are and each of which is linked to other synsets by means of a small number of conceptual relations. The dataset includes 6, reviews,pictures,businesses from 10 metropolitan areas. A lover of music, writing and learning something out of the box.

Contact: ambika. The list is in alphabetical order. Share This.Last Updated on December 11, It is a good idea to have small well understood datasets when getting started in machine learning and learning a new tool. The Weka machine learning workbench provides a directory of small well understood datasets in the installed directory.

In this post you will discover some of these small well understood datasets distributed with Weka, their details and where to learn more about them.

We will focus on a handful of datasets of differing types.

Binary Classification on Imbalanced Dataset, by Xingyu Wang&Zhenyu Chen

After reading this post you will know:. Discover how to prepare data, fit models, and evaluate their predictions, all without writing a line of code in my new bookwith 18 step-by-step tutorials and 3 projects with Weka. This is very useful when you are getting started in machine learning or learning how to get started with the Weka platform. It provides standard machine learning datasets for common classification and regression problems, for example, below is a snapshot from this directory:.

Provided Datasets in Weka Installation Directory. All datasets are in the Weka native ARFF file format and can be loaded directly into Weka, meaning you can start developing practice models immediately.

If you have chosen to install one of these distributions, you can download the.

Miscellaneous collections of datasets

Binary classification is where the output variable to be predicted is nominal comprised of two classes. This is perhaps the most well studied type of predictive modeling problem and the type of problem that is good to start with. There are many classification type problems, where the output variable has more than two classes.

These are called multi-class classification problems. This is a good type of problem to look at after you have some confidence with binary classification. Regression is an important class of predictive modeling problem.

It is available from the datasets page on the Weka web page and is the first in the list called:.

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It is a. You should be able to unzip it with most modern unzip programs. If you have Java installed which you very likely do to use Wekayou can also unzip the. Unzipping the file will create a new directory called numeric that contains 37 regression datasets in ARFF native Weka format. In this post you discovered the standard machine learning datasets distributed with the Weka machine learning platform.

Do you have any questions about standard machine learning datasets in Weka or about this post? Ask your questions in the comments and I will do my best to answer. Covers self-study tutorials and end-to-end projects like: Loading datavisualizationbuild modelstuningand much more


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