DEVELOPMENT...      {
    "data_id": "43477",
    "name": "Swiss-banknote-conterfeit-detection",
    "exact_name": "Swiss-banknote-conterfeit-detection",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nWill you be able to identify genuine and conterfeit banknotes, even if half of the data is conterfeit? Perfect for testing different outlier detection algorithms.\nContent\nThe dataset includes information about the shape of the bill, as well as the label. It is made up of 200 banknotes in total, 100 for genuine\/conterfeit each.\nAttributes:\n-conterfeit: Wether a banknote is conterfeit (1) or genuine (0)\n-Length: Length of bill (mm)\n-Left: Width of left edge (mm)\n-Right: Width of right edge (mm)\n-Bottom: Bottom margin width (mm)\n-Top: Top margin width (mm)\n-Diagonal: Length of diagonal (mm)\nOriginal Data Source\nFlury, B. and Riedwyl, H. (1988). Multivariate Statistics: A practical approach. London: Chapman  Hall, Tables 1.1 and 1.2, pp. 5-8.\nApplications\nWhile it might be pretty easy for a classifier to decide wether the banknotes are conterfeit or not, what about methods using outlier detection?\nClassical methods of outlier detection won't work, since half of the data consist of outliers (conterfeit bills), so more robust methods will be needed.",
    "format": "arff",
    "uploader": " Lowe",
    "uploader_id": 30126,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 13:26:21",
    "update_comment": null,
    "last_update": "2022-03-23 13:26:21",
    "licence": "CC BY-NC-SA 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102302\/dataset",
    "default_target_attribute": "conterfeit",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Swiss-banknote-conterfeit-detection",
            "Context Will you be able to identify genuine and conterfeit banknotes, even if half of the data is conterfeit? Perfect for testing different outlier detection algorithms. Content The dataset includes information about the shape of the bill, as well as the label. It is made up of 200 banknotes in total, 100 for genuine\/conterfeit each. Attributes: -conterfeit: Wether a banknote is conterfeit (1) or genuine (0) -Length: Length of bill (mm) -Left: Width of left edge (mm) -Right: Width of right edge "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 200,
        "NumberOfFeatures": 7,
        "NumberOfClasses": 0,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 7,
        "NumberOfSymbolicFeatures": 0,
        "PercentageOfSymbolicFeatures": 0,
        "AutoCorrelation": 0.9949748743718593,
        "PercentageOfNumericFeatures": 100,
        "PercentageOfMissingValues": 0,
        "PercentageOfInstancesWithMissingValues": 0,
        "PercentageOfBinaryFeatures": 0,
        "NumberOfBinaryFeatures": 0,
        "MinorityClassSize": null,
        "MinorityClassPercentage": null,
        "MajorityClassSize": null,
        "MajorityClassPercentage": null,
        "Dimensionality": 0.035
    },
    "tags": [],
    "features": [
        {
            "name": "conterfeit",
            "index": "0",
            "type": "numeric",
            "distinct": "2",
            "missing": "0",
            "target": "1",
            "min": "0",
            "max": "1",
            "mean": "1",
            "stdev": "1"
        },
        {
            "name": "Length",
            "index": "1",
            "type": "numeric",
            "distinct": "21",
            "missing": "0",
            "min": "214",
            "max": "216",
            "mean": "215",
            "stdev": "0"
        },
        {
            "name": "Left",
            "index": "2",
            "type": "numeric",
            "distinct": "17",
            "missing": "0",
            "min": "129",
            "max": "131",
            "mean": "130",
            "stdev": "0"
        },
        {
            "name": "Right",
            "index": "3",
            "type": "numeric",
            "distinct": "19",
            "missing": "0",
            "min": "129",
            "max": "131",
            "mean": "130",
            "stdev": "0"
        },
        {
            "name": "Bottom",
            "index": "4",
            "type": "numeric",
            "distinct": "50",
            "missing": "0",
            "min": "7",
            "max": "13",
            "mean": "9",
            "stdev": "1"
        },
        {
            "name": "Top",
            "index": "5",
            "type": "numeric",
            "distinct": "35",
            "missing": "0",
            "min": "8",
            "max": "12",
            "mean": "11",
            "stdev": "1"
        },
        {
            "name": "Diagonal",
            "index": "6",
            "type": "numeric",
            "distinct": "42",
            "missing": "0",
            "min": "138",
            "max": "142",
            "mean": "140",
            "stdev": "1"
        }
    ],
    "nr_of_issues": 0,
    "nr_of_downvotes": 0,
    "nr_of_likes": 0,
    "nr_of_downloads": 0,
    "total_downloads": 0,
    "reach": 0,
    "reuse": 0,
    "impact_of_reuse": 0,
    "reach_of_reuse": 0,
    "impact": 0
}