Decision-tree induction to detect clinical mastitis with automatic milking

Computers and Electronics in Agriculture

Volumn 70, Issue 1, 2010, Pages 60-68

  Kamphuis, C ^a   Mollenhorst, H ^a   Feelders, A ^a   Pietersma, D ^a   Hogeveen, H ^a,b  

^a UTRECHT UNIVERSITY   (Netherlands)

^b WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

Automatic mastitis detection; Data mining; Robotic milking

Indexed keywords

10-FOLD CROSS-VALIDATION; AUTOMATIC MILKING; CLINICAL MASTITIS; COST MATRICES; DAIRY HERDS; DECISION-TREE ALGORITHM; DECISION-TREE INDUCTION; DEFAULT PARAMETERS; DETECTION PERFORMANCE; DIAGNOSTIC ABILITY; ELECTRICAL CONDUCTIVITY; FALSE NEGATIVES; GOLD STANDARDS; PARTIAL AREA; PERFORMANCE CHARACTERISTICS; PROBABILITY ESTIMATE; RECEIVER OPERATING CHARACTERISTIC CURVES; ROBOTIC MILKING; SENSITIVITY AND SPECIFICITY; SENSOR DATA;

DATA MINING; ELECTRIC CONDUCTIVITY; GOLD; ROBOTICS; SENSORS; STANDARDS;

DECISION TREES;

AGRICULTURAL TECHNOLOGY; COLOR; DATA MINING; DECISION ANALYSIS; ELECTRICAL CONDUCTIVITY; FARMERS KNOWLEDGE; MILK PRODUCTION; PARAMETERIZATION; ROBOTICS; SENSITIVITY ANALYSIS; SENSOR;

EID: 72149085456     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2009.08.012     Document Type: Article

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