McQueen, R.J., Garner, S.R., Nevill-Manning, C.G., and Witten, I.G. Applying machine learning to agricultural data. Computers and Electronics in Agriculture (12) 1995, pp. 275293.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....rather than where decision tree researchers tested their algorithm(s) on several application domains. The application areas are listed below in alphabetical order. ffl Agriculture: Application of a range of machine learning methods to problems in agriculture and horticulture is described in [316]. ffl Astronomy: Astronomy has been an active domain for using automated classification techniques. 22 Use of decision trees for filtering noise from Hubble Space Telescope images was reported recently in [424] Decision trees have helped in star galaxy classification [500] determining galaxy ....

R.J. McQueen, S. R. Garner, C.G. Nevill-Manning, and I.H. Witten. Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 12(4):275--293, June 1995.

....decision tree researchers tested their algorithm(s) on several application domains. The application areas are listed below in alphabetical order. ffl Agriculture: Application of a range of machine learning methods including decision trees to problems in agriculture and horticulture is described in [239]. ffl Astronomy: Astronomy has been an active domain for using automated classification techniques. 19 Use of decision trees has been reported for filtering noise from Hubble Space Telescope images [323] in star galaxy classification [378] for determining galaxy counts [377] and discovering ....

R.J. McQueen, S. R. Garner, C.G. Nevill-Manning, and I.H. Witten. Applying machine learning to agricultural data. Comp. and Electronics in Agriculture, 12(4):275--293, June 1995.

.... methods to this task: C4.5 (Quinlan, 1992) and FOIL (Quinlan, 1990) These methods were chosen because they are well understood among machine learning researchers and practitioners and because 3 they are readily available as part of the WEKA machine learning workbench (Holmes et al. 1994; McQueen et al. 1994a) Milk yield data collected from a Dairying Research Corporation (DRC) herd of 120 cows at the Ruakura Agricultural Research Station for the 1993 94 milking season were used. This body of data was used to answer the following research questions: First, is it possible to determine oestrus events ....

....by tail paint may have happened during any of the preceding seven days. If a program can determine the actual day of the oestrus, that will enable the farmer to more closely anticipate the date of the subsequent oestrus. This study was undertaken as part of the WEKA machine learning project (McQueen et al. 1994a; McQueen et al. 1994b; Mitchell, 1995; De War et al. 1994) at the University of Waikato (WEKA is an acronym for the Waikato Environment for Knowledge Analysis) One of the primary objectives of the WEKA project is to investigate the application of machine learning techniques to problems in ....

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McQueen, R.J., Garner, S.R., Nevill-Manning, C.G. and Witten, I.H. (1994) Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 12 :275--293.

....would be useful when classifying examples containing time series data. These particular methods were chosen because they are well understood among machine learning researchers and are also readily available as part of the WEKA (Waikato Environment for Knowledge Analysis) machine learning workbench (McQueen et al., 1994). WEKA integrates a wide range of machine learning algorithms and support tools into a single interactive package, allowing data to be analysed using different learning systems and the results to be evaluated in a consistent manner. Milking performance data was collected from a Dairying Research ....

McQueen, R.J., Garner, S.R., Nevill-Manning, C.G. and Witten, I.H. (1994) Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 12: 275--293.

....as concepts, decision trees, discrimination nets or production rules. A common goal of such methods is to predict the value of an attribute in the data based on other attributes. The output can then be used, by either a human or a system, to classify unseen examples. Good surveys can be found in [1] and [2] Outlook Temp. F Hum idity Windy Play sunny 85 85 false Don t Play sunny 80 90 true Don t Play overcast 83 78 false Play rain 70 96 false Play rain 68 80 false Play rain 65 70 true Don t Play overcast 64 65 true Play sunny 72 95 false Don t Play sunny 69 70 false Play rain 75 80 ....

R. J. McQueen, S. R. Garner, C. G NevillManning and I. H. Witten, "Applying machine learning to agricultural data", Journal of Computing and Electronics in Agriculture, Vol 12(4), pp 275293, 1995.

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McQueen, R.J., Garner, S.R., Nevill-Manning, C.G., and Witten, I.G. Applying machine learning to agricultural data. Computers and Electronics in Agriculture (12) 1995, pp. 275293.

....of multiple and unclassified instances the predictions of percentage correct and S f may depart markedly. This point is emphasised by the results in Figure 2. This shows the plot of percentage correct versus S f on dataset wcr , one dataset from an agricultural problem involving cowculling (McQueen et al. 1995). The data consists of some 2000 instances of data about a single farmer s cows over a period of five years. As well as data about the cows milk production, breeding and other attributes, it also records which cows were culled from the herd and which died from other causes. The goal is to ....

McQueen, R.J., Garner, S.R., Nevill-Manning, C.G. and Witten, I.H. (1995) "Applying machine learning to agricultural data." J Computing and Electronics in Agriculture, 12(4), 275--293.

....the areas of agriculture, machine learning research and education. Agricultural The most significant project so far carried out using the WEKA workbench has been the analysis of dairy herd data for the purposes of isolating rules that describe factors that farmers might use for culling decisions [10]. This involved working with a large data set of 19 103 records containing 705 attributes spread across 10 herds and 6 years. About 40 new attributes were derived, including attributes like age and production index relative to herd, and these were added to the original data set which was then ....

McQueen, R.J., Garner, S.R., Nevill-Manning, C.G. and Witten, I.H. Applying Machine Learning to Agricultural Data. To be published in Computers and Electronics, Elsevier Science Publishers, Amsterdam.

....FROM RAW TO CLEAN DATA Having obtained the raw data, it must be massaged into a form suitable for processing by the automated tools. In the case of the WEKA system, the data is extracted and translated into a standard format we call ARFF, for Attribute Relation File Format (Holmes et al. 1994; McQueen et al. 1994). This generally involves taking the physical extract of a database and processing it through a series of steps to generate an ARFF dataset. Anomalies arise in this process and must be resolved via consultation with the data provider. This may result in new data being generated, or in a better ....

McQueen, R.J., Garner, S.R., Nevill-Manning, C.G., and Witten, I.H. (1994) "Applying machine learning to agricultural data." In Press, Journal of Computing and Electronics in Agriculture. Also available as Working Paper Series 94/3 Department of Computer Science, University of Waikato (Hamilton, New Zealand).

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R.J. McQueen, S. R. Garner, C.G. Nevill-Manning, and I.H. Witten. Applying machine learning to agricultural data. Comp. and Electronics in Agriculture, 12#4#:275#293, June 1995.

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