Simplifying decision trees:
Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questio...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass.
1986
|
| Schriftenreihe: | Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo
930. |
| Schlagworte: | |
| Zusammenfassung: | Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees without compromising their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains. |
| Beschreibung: | 16 S. |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV007648604 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | t | ||
| 008 | 930421s1986 |||| 00||| eng d | ||
| 035 | |a (OCoLC)227704104 | ||
| 035 | |a (DE-599)BVBBV007648604 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-355 | ||
| 084 | |a SS 4860 |0 (DE-625)143532: |2 rvk | ||
| 100 | 1 | |a Quinlan, J. R. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Simplifying decision trees |
| 264 | 1 | |a Cambridge, Mass. |c 1986 | |
| 300 | |a 16 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo |v 930. | |
| 520 | 3 | |a Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees without compromising their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains. | |
| 650 | 4 | |a Decision Trees | |
| 650 | 7 | |a Accuracy |2 dtict | |
| 650 | 7 | |a Artificial intelligence |2 dtict | |
| 650 | 7 | |a Cybernetics |2 scgdst | |
| 650 | 7 | |a Decision making |2 dtict | |
| 650 | 7 | |a Decision theory |2 dtict | |
| 650 | 7 | |a Fault tree analysis |2 dtict | |
| 650 | 7 | |a Information processing |2 dtict | |
| 650 | 7 | |a Operations Research |2 scgdst | |
| 650 | 7 | |a Test beds |2 dtict | |
| 650 | 4 | |a Künstliche Intelligenz | |
| 830 | 0 | |a Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo |v 930. |w (DE-604)BV006654788 |9 930 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-005011767 | ||
Datensatz im Suchindex
| _version_ | 1804121997983088640 |
|---|---|
| any_adam_object | |
| author | Quinlan, J. R. |
| author_facet | Quinlan, J. R. |
| author_role | aut |
| author_sort | Quinlan, J. R. |
| author_variant | j r q jr jrq |
| building | Verbundindex |
| bvnumber | BV007648604 |
| classification_rvk | SS 4860 |
| ctrlnum | (OCoLC)227704104 (DE-599)BVBBV007648604 |
| discipline | Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01928nam a2200421 cb4500</leader><controlfield tag="001">BV007648604</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">930421s1986 |||| 00||| eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)227704104</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV007648604</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-355</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SS 4860</subfield><subfield code="0">(DE-625)143532:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Quinlan, J. R.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Simplifying decision trees</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, Mass.</subfield><subfield code="c">1986</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">16 S.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo</subfield><subfield code="v">930.</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees without compromising their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision Trees</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Accuracy</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Artificial intelligence</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Cybernetics</subfield><subfield code="2">scgdst</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Decision making</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Decision theory</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fault tree analysis</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Information processing</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Operations Research</subfield><subfield code="2">scgdst</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Test beds</subfield><subfield code="2">dtict</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Künstliche Intelligenz</subfield></datafield><datafield tag="830" ind1=" " ind2="0"><subfield code="a">Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo</subfield><subfield code="v">930.</subfield><subfield code="w">(DE-604)BV006654788</subfield><subfield code="9">930</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-005011767</subfield></datafield></record></collection> |
| id | DE-604.BV007648604 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:06:53Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-005011767 |
| oclc_num | 227704104 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | 16 S. |
| publishDate | 1986 |
| publishDateSearch | 1986 |
| publishDateSort | 1986 |
| record_format | marc |
| series | Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo |
| series2 | Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo |
| spelling | Quinlan, J. R. Verfasser aut Simplifying decision trees Cambridge, Mass. 1986 16 S. txt rdacontent n rdamedia nc rdacarrier Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo 930. Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees without compromising their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains. Decision Trees Accuracy dtict Artificial intelligence dtict Cybernetics scgdst Decision making dtict Decision theory dtict Fault tree analysis dtict Information processing dtict Operations Research scgdst Test beds dtict Künstliche Intelligenz Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo 930. (DE-604)BV006654788 930 |
| spellingShingle | Quinlan, J. R. Simplifying decision trees Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo Decision Trees Accuracy dtict Artificial intelligence dtict Cybernetics scgdst Decision making dtict Decision theory dtict Fault tree analysis dtict Information processing dtict Operations Research scgdst Test beds dtict Künstliche Intelligenz |
| title | Simplifying decision trees |
| title_auth | Simplifying decision trees |
| title_exact_search | Simplifying decision trees |
| title_full | Simplifying decision trees |
| title_fullStr | Simplifying decision trees |
| title_full_unstemmed | Simplifying decision trees |
| title_short | Simplifying decision trees |
| title_sort | simplifying decision trees |
| topic | Decision Trees Accuracy dtict Artificial intelligence dtict Cybernetics scgdst Decision making dtict Decision theory dtict Fault tree analysis dtict Information processing dtict Operations Research scgdst Test beds dtict Künstliche Intelligenz |
| topic_facet | Decision Trees Accuracy Artificial intelligence Cybernetics Decision making Decision theory Fault tree analysis Information processing Operations Research Test beds Künstliche Intelligenz |
| volume_link | (DE-604)BV006654788 |
| work_keys_str_mv | AT quinlanjr simplifyingdecisiontrees |