Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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- Bu səhifədəki naviqasiya:
- Figure 1.3
- Table 1.7 The soybean data.
1 . 2 S I M P L E E X A M P L E S : T H E W E AT H E R P RO B L E M A N D OT H E R S 1 9 wage increase first year bad ≤ 2.5 statutory holidays > 2.5
good > 10
wage increase first year ≤ 10 bad ≤ 4 good > 4
(a) ≤ 2.5 statutory holidays > 2.5
bad ≤ 36 health plan contribution > 36
good > 10
wage increase first year wage increase first year working hours per week
≤ 10
bad none
good half
bad full
bad ≤ 4 good > 4
(b) Figure 1.3 Decision t rees for the labor negotiations data. P088407-Ch001.qxd 4/30/05 11:11 AM Page 19 680 examples, each representing a diseased plant. Plants were measured on 35 attributes, each one having a small set of possible values. Examples are labeled with the diagnosis of an expert in plant biology: there are 19 disease categories altogether—horrible-sounding diseases such as diaporthe stem canker, rhizoc- tonia root rot, and bacterial blight, to mention just a few. Table 1.7 gives the attributes, the number of different values that each can have, and a sample record for one particular plant. The attributes are placed into different categories just to make them easier to read. Here are two example rules, learned from this data: If [leaf condition is normal and stem condition is abnormal and stem cankers is below soil line and canker lesion color is brown] then
diagnosis is rhizoctonia root rot If [leaf malformation is absent and stem condition is abnormal and stem cankers is below soil line and canker lesion color is brown] then
diagnosis is rhizoctonia root rot These rules nicely illustrate the potential role of prior knowledge—often called domain knowledge—in machine learning, because the only difference between the two descriptions is leaf condition is normal versus leaf malformation is absent. Now, in this domain, if the leaf condition is normal then leaf malfor- mation is necessarily absent, so one of these conditions happens to be a special case of the other. Thus if the first rule is true, the second is necessarily true as well. The only time the second rule comes into play is when leaf malformation is absent but leaf condition is not normal, that is, when something other than malformation is wrong with the leaf. This is certainly not apparent from a casual reading of the rules. Research on this problem in the late 1970s found that these diagnostic rules could be generated by a machine learning algorithm, along with rules for every other disease category, from about 300 training examples. These training examples were carefully selected from the corpus of cases as being quite differ- ent from one another—“far apart” in the example space. At the same time, the plant pathologist who had produced the diagnoses was interviewed, and his expertise was translated into diagnostic rules. Surprisingly, the computer- generated rules outperformed the expert-derived rules on the remaining test examples. They gave the correct disease top ranking 97.5% of the time com- pared with only 72% for the expert-derived rules. Furthermore, not only did 2 0
C H A P T E R 1 | W H AT ’ S I T A L L A B O U T ? P088407-Ch001.qxd 4/30/05 11:11 AM Page 20 1 . 2 S I M P L E E X A M P L E S : T H E W E AT H E R P RO B L E M A N D OT H E R S 2 1
Number
Attribute of values Sample value Environment time of occurrence 7 July precipitation 3 above normal temperature 3 normal cropping history 4 same as last year hail damage 2 yes damaged area 4 scattered severity 3 severe plant height 2 normal plant growth 2 abnormal seed treatment 3 fungicide germination 3 less than 80% Seed condition 2 normal
mold growth 2 absent discoloration 2 absent size 2 normal shriveling 2 absent Fruit condition of fruit pods 3 normal
fruit spots 5 — Leaf condition 2 abnormal
leaf spot size 3 — yellow leaf spot halo 3 absent leaf spot margins 3 — shredding 2 absent leaf malformation 2 absent leaf mildew growth 3 absent Stem condition 2 abnormal
stem lodging 2 yes stem cankers 4 above soil line canker lesion color 3 — fruiting bodies on stems 2 present external decay of stem 3 firm and dry mycelium on stem 2 absent internal discoloration 3 none sclerotia 2 absent Root condition 3 normal
Diagnosis diaporthe stem 19 canker
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