LIBSVM Data: Classification (Binary Class)

This page contains many classification, regression, and multi-label data sets used in our papers. Many are from UCI, Statlog, StatLib and other collections. We really thank their efforts. For most sets, we directly transform the file into LIBSVM format and linearly scale each attribute to [-1,1]. The testing data (if provided) is adjusted accordingly. Some training data are further separated to "training" (tr) and "validation" (val) sets. Details can be found in the description of each data set.

a1a

Source: UCI / Adult
Preprocessing: The original Adult data set has 14 features, among which six are continuous and eight are categorical. In this data set, continuous features are discretized into quantiles, and each quantile is represented by a binary feature. Also, a categorical feature with m categories is converted to m binary features. Details on how each feature is converted can be found in the beginning of each file from this page. [JP98a]
# of classes: 2
# of data: 1,605 / 30,956 (testing)
# of features: 123 / 123 (testing)
Files:
- a1a
- a1a.t (testing)

a2a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 2,265 / 30,296 (testing)
# of features: 123 / 123 (testing)
Files:
- a2a
- a2a.t (testing)

a3a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 3,185 / 29,376 (testing)
# of features: 123 / 123 (testing)
Files:
- a3a
- a3a.t (testing)

a4a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 4,781 / 27,780 (testing)
# of features: 123 / 123 (testing)
Files:
- a4a
- a4a.t (testing)

a5a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 6,414 / 26,147 (testing)
# of features: 123 / 123 (testing)
Files:
- a5a
- a5a.t (testing)

a6a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 11,220 / 21,341 (testing)
# of features: 123 / 123 (testing)
Files:
- a6a
- a6a.t (testing)

a7a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 16,100 / 16,461 (testing)
# of features: 123 / 123 (testing)
Files:
- a7a
- a7a.t (testing)

a8a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 22,696 / 9,865 (testing)
# of features: 123 / 123 (testing)
Files:
- a8a
- a8a.t (testing)

a9a

Source: UCI / Adult
Preprocessing: The same as a1a. [JP98a]
# of classes: 2
# of data: 32,561 / 16,281 (testing)
# of features: 123 / 123 (testing)
Files:
- a9a
- a9a.t (testing)

australian

Source: Statlog / Australian
# of classes: 2
# of data: 690
# of features: 14
Files:
- australian
- australian_scale (scaled to [-1,1])

breast-cancer

Source: UCI / Wisconsin Breast Cancer
Preprocessing: Note that the original data has the column 1 containing sample ID. Also 16 instances with missing values are removed.
# of classes: 2
# of data: 683
# of features: 10
Files:
- breast-cancer
- breast-cancer_scale (scaled to [-1,1])

cod-rna

Source: [AVU06a]
Features:
1. Divide by 10 to get deltaG_total value computed by the Dynalign algorithm
2. The length of shorter sequence
3. 'A' frequencies of sequence 1
4. 'U' frequencies of sequence 1
5. 'C' frequencies of sequence 1
6. 'A' frequencies of sequence 2
7. 'U' frequencies of sequence 2
8. 'C' frequencies of sequence 2
# of classes: 2
# of data: 59535 / 271617 (validation) / 157413 (unused/remaining)
# of features: 8
Files:
- cod-rna (training)
- cod-rna.t (validation)
- cod-rna.r (unused/remaining)

colon-cancer

Source: [AU99a]
Preprocessing: Instance-wise normalization to mean zero and variance one. Then feature-wise normalization to mean zero and variance one. [SKS03a]
# of classes: 2
# of data: 62
# of features: 2,000
Files:
- colon-cancer.bz2

covtype.binary

Source: UCI / Covertype
Preprocessing: Transform from multiclass into binary class. [RC02a]
# of classes: 2
# of data: 581,012
# of features: 54
Files:
- covtype.libsvm.binary.bz2
- covtype.libsvm.binary.scale.bz2 (scaled to [0,1])

diabetes

Source: UCI / Pima Indians Diabetes
# of classes: 2
# of data: 768
# of features: 8
Files:
- diabetes
- diabetes_scale (scaled to [-1,1])

duke breast-cancer

Source: [MW01a]
Preprocessing: Instance-wise normalization to mean zero and variance one. Then feature-wise normalization to mean zero and variance one. The original dataset consists of 49 instances. Five are removed since the classification results using immunohistochemistry and protein immunoblotting assay confilcted. Of the remaining, two instances were rejected due to failed array hybridization. The rest data are further splited into training (38), and validation (4). [SKS03a]
# of classes: 2
# of data: 44
# of features: 7,129
Files:
- duke.bz2
- duke.tr.bz2 (tr)
- duke.val.bz2 (val)

fourclass

Source: [TKH96a]
Preprocessing: transform to two-class
# of classes: 2
# of data: 862
# of features: 2
Files:
- fourclass
- fourclass_scale (scaled to [-1,1])

german.numer

Source: Statlog / German
# of classes: 2
# of data: 1,000
# of features: 24
Files:
- german.numer
- german.numer_scale (scaled to [-1,1])

heart

Source: Statlog / Heart
# of classes: 2
# of data: 270
# of features: 13
Files:
- heart
- heart_scale (scaled to [-1,1])

ijcnn1

Source: [DP01a]
Preprocessing: We use winner's transformation [Chang01d]
# of classes: 2
# of data: 49,990 / 91,701 (testing)
# of features: 22
Files:
- ijcnn1.bz2
- ijcnn1.t.bz2 (testing)
- ijcnn1.tr.bz2 (tr)
- ijcnn1.val.bz2 (val)

ionosphere

Source: UCI / Ionosphere
# of classes: 2
# of data: 351
# of features: 34
Files:
- ionosphere_scale (scaled to [-1,1])

leukemia

Source: [TG99a]
Preprocessing: Merge training/testing. Instance-wise normalization to mean zero and variance one. Then feature-wise normalization to mean zero and variance one. [SKS03a]
# of classes: 2
# of data: 38 / 34 (testing)
# of features: 7129
Files:
- leu.bz2
- leu.t.bz2 (testing)

liver-disorders

Source: UCI / Liver-disorders
# of classes: 2
# of data: 345
# of features: 6
Files:
- liver-disorders
- liver-disorders_scale (scaled to [-1,1])

mushrooms

Source: UCI / mushrooms
Preprocessing: Each nominal attribute is expaned into several binary attributes. The original attribute #12 has missing values and is not used.
# of classes: 2
# of data: 8124
# of features: 112
Files:
- mushrooms

news20.binary

Source: [SSK05a]
Preprocessing: Each instance has unit length.
# of classes: 2
# of data: 19,996
# of features: 1,355,191
Files:
- news20.binary.bz2

rcv1.binary

Source: [DL04b]
Preprocessing: positive: CCAT, ECAT; negative: GCAT, MCAT; instances in both positive and negative classes are removed.
# of classes: 2
# of data: 20,242 / 677,399 (testing)
# of features: 47,236
Files:
- rcv1_train.binary.bz2
- rcv1_test.binary.bz2 (testing)

real-sim

Source: A. McCallum / Real vs. Simulated
Preprocessing: Vikas Sindhwani for the SVMlin project
# of classes: 2
# of data: 72,309
# of features: 20,958
Files:
- real-sim.bz2

splice

Source: Delve / splice
# of classes: 2
# of data: 1,000 / 2,175 (testing)
# of features: 60
Files:
- splice
- splice_scale (scaled to [-1,1])
- splice.t (testing)

sonar

Source: UCI / Undocumented/Sonar
# of classes: 2
# of data: 208
# of features: 60
Files:
- sonar_scale (scaled to [-1,1])

svmguide1

Source: [CWH03a]
Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University, Sweden.
# of classes: 2
# of data: 3,089 / 4,000 (testing)
# of features: 4
Files:
- svmguide1
- svmguide1.t (testing)

svmguide3

Source: [CWH03a]
Preprocessing: Original data: someone from Germany working with the car industry.
# of classes: 2
# of data: 1,243 / 41 (testing)
# of features: 21
Files:
- svmguide3
- svmguide3.t (testing)

w1a

Source: [JP98a]
# of classes: 2
# of data: 2,477 / 47,272 (testing)
# of features: 300 / 300 (testing)
Files:
- w1a
- w1a.t (testing)

w2a

Source: [JP98a]
# of classes: 2
# of data: 3,470 / 46,279 (testing)
# of features: 300 / 300 (testing)
Files:
- w2a
- w2a.t (testing)

w3a

Source: [JP98a]
# of classes: 2
# of data: 4,912 / 44,837 (testing)
# of features: 300 / 300 (testing)
Files:
- w3a
- w3a.t (testing)

w4a

Source: [JP98a]
# of classes: 2
# of data: 7,366 / 42,383 (testing)
# of features: 300 / 300 (testing)
Files:
- w4a
- w4a.t (testing)

w5a

Source: [JP98a]
# of classes: 2
# of data: 9,888 / 39,861 (testing)
# of features: 300 / 300 (testing)
Files:
- w5a
- w5a.t (testing)

w6a

Source: [JP98a]
# of classes: 2
# of data: 17,188 / 32,561 (testing)
# of features: 300 / 300 (testing)
Files:
- w6a
- w6a.t (testing)

w7a

Source: [JP98a]
# of classes: 2
# of data: 24,692 / 25,057 (testing)
# of features: 300 / 300 (testing)
Files:
- w7a
- w7a.t (testing)

w8a

Source: [JP98a]
# of classes: 2
# of data: 49,749 / 14,951 (testing)
# of features: 300 / 300 (testing)
Files:
- w8a
- w8a.t (testing)

webspam

Source: Webb Spam Corpus [ST06a]
Preprocessing: We consider the subset used in the Pascal Large Scale Learning Challenge. According to Soeren Sonnenburg, all positive examples were taken and the negative examples were created by randomly traversing the Internet starting at well known (e.g. news) web-sites. We treat continuous n bytes as a word: trigram if n = 3 and unigram if n = 1. We use word count as the feature value and normalize each instance to unit length. For unigram, the number of features is 254. Contact us if you need scripts to obtain the data from the original documents.
# of classes: 2
# of data: 350,000
# of features: 16,609,143
Files:
- webspam_wc_normalized_trigram.svm.bz2 (Tri-gram)
- webspam_wc_normalized_unigram.svm.bz2 (Uni-gram)