Data
eye_movements

eye_movements

active ARFF Publicly available Visibility: public Uploaded 27-01-2023 by Young Lee
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Jarkko Salojarvi, Kai Puolamaki, Jaana Simola, Lauri Kovanen, Ilpo Kojo, Samuel Kaski. Inferring Relevance from Eye Movements: Feature Extraction. Helsinki University of Technology, Publications in Computer and Information Science, Report A82. 3 March 2005. Data set at http://www.cis.hut.fi/eyechallenge2005/Competition 1 (preprocessed data) A straight-forward classification task. We provide pre-computed feature vectors for each word in the eye movement trajectory, with class labels.The dataset consist of several assignments. Each assignment consists of a question followed by ten sentences (titles of news articles). One of the sentences is the correct answer to the question (C) and five of the sentences are irrelevant to the question (I). Four of the sentences are relevant to the question (R), but they do not answer it.

24 features

class (target)numeric2 unique values
0 missing
lineNonumeric7608 unique values
0 missing
assgNonumeric331 unique values
0 missing
prevFixDurnumeric58 unique values
0 missing
firstfixDurnumeric59 unique values
0 missing
firstPassFixDurnumeric94 unique values
0 missing
nextFixDurnumeric62 unique values
0 missing
firstSaccLennumeric6792 unique values
0 missing
lastSaccLennumeric6977 unique values
0 missing
prevFixPosnumeric5911 unique values
0 missing
landingPosnumeric5390 unique values
0 missing
leavingPosnumeric5458 unique values
0 missing
totalFixDurnumeric105 unique values
0 missing
meanFixDurnumeric166 unique values
0 missing
regressLennumeric431 unique values
0 missing
regressDurnumeric249 unique values
0 missing
pupilDiamMaxnumeric3058 unique values
0 missing
pupilDiamLagnumeric2158 unique values
0 missing
timePrtctgnumeric843 unique values
0 missing
titleNonumeric10 unique values
0 missing
wordNonumeric10 unique values
0 missing
P1stFixationnominal2 unique values
0 missing
P2stFixationnominal2 unique values
0 missing
nextWordRegressnominal2 unique values
0 missing

19 properties

7608
Number of instances (rows) of the dataset.
24
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
21
Number of numeric attributes.
3
Number of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
3
Number of binary attributes.
12.5
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
87.5
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
12.5
Percentage of nominal attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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