Item talk:Q236547
From geokb
{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "A cross-validation package driving Netica with python",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "70128127",
"url": "https://pubs.usgs.gov/publication/70128127"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70128127
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.1016/j.envsoft.2014.09.007",
"url": "https://doi.org/10.1016/j.envsoft.2014.09.007"
}
],
"journal": {
"@type": "Periodical",
"name": "Environmental Modelling and Software",
"volumeNumber": "63",
"issueNumber": null
},
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Environmental Modelling and Software"
}
],
"datePublished": "2014",
"dateModified": "2014-10-03",
"abstract": "Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica is open-source, written in Python, and extends the Netica software package to perform cross-validation and read, rebuild, and learn BNs from data. Insights gained from cross-validation and implications on prediction versus description are illustrated with: a data-driven oceanographic application; and a model-emulation application. These examples show that overfitting occurs when BNs become more complex than allowed by supporting data and overfitting incurs computational costs as well as causing a reduction in prediction skill. CVNetica evaluates overfitting using several complexity metrics (we used level of discretization) and its impact on performance metrics (we used skill).",
"description": "10 p.",
"publisher": {
"@type": "Organization",
"name": "Elsevier"
},
"author": [
{
"@type": "Person",
"name": "Fienen, Michael N. mnfienen@usgs.gov",
"givenName": "Michael N.",
"familyName": "Fienen",
"email": "mnfienen@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-7756-4651",
"url": "https://orcid.org/0000-0002-7756-4651"
},
"affiliation": [
{
"@type": "Organization",
"name": "Wisconsin Water Science Center",
"url": "https://www.usgs.gov/centers/upper-midwest-water-science-center"
}
]
},
{
"@type": "Person",
"name": "Plant, Nathaniel G. nplant@usgs.gov",
"givenName": "Nathaniel G.",
"familyName": "Plant",
"email": "nplant@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-5703-5672",
"url": "https://orcid.org/0000-0002-5703-5672"
},
"affiliation": [
{
"@type": "Organization",
"name": "St. Petersburg Coastal and Marine Science Center",
"url": "https://www.usgs.gov/centers/spcmsc"
},
{
"@type": "Organization",
"name": "Office of the AD Hazards",
"url": "https://www.usgs.gov/mission-areas/natural-hazards"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "Wisconsin Water Science Center",
"url": "https://www.usgs.gov/centers/upper-midwest-water-science-center"
}
]
},
"OpenAlex": {
"abstract_inverted_index": {
"Bayesian": [
0
],
"networks": [
1
],
"(BNs)": [
2
],
"are": [
3,
88
],
"powerful": [
4
],
"tools": [
5,
47
],
"for": [
6,
33
],
"probabilistically": [
7
],
"simulating": [
8
],
"natural": [
9
],
"systems": [
10
],
"and": [
11,
61,
70,
73,
82,
95,
115,
141
],
"emulating": [
12
],
"process": [
13
],
"models.": [
14
],
"Cross": [
15
],
"validation": [
16
],
"is": [
17,
35,
56
],
"a": [
18,
43,
91,
96,
124
],
"technique": [
19
],
"to": [
20,
42,
49,
67
],
"avoid": [
21
],
"overfitting": [
22,
103,
116,
131
],
"resulting": [
23
],
"from": [
24,
76,
80
],
"overly": [
25
],
"complex": [
26,
109
],
"BNs.": [
27
],
"Overfitting": [
28
],
"reduces": [
29
],
"predictive": [
30
],
"skill.": [
31,
128
],
"Cross-validation": [
32
],
"BNs": [
34,
75,
106
],
"known": [
36
],
"but": [
37
],
"rarely": [
38
],
"implemented": [
39
],
"due": [
40
],
"partly": [
41
],
"lack": [
44
],
"of": [
45,
139
],
"software": [
46,
65
],
"designed": [
48
],
"work": [
50
],
"with": [
51
],
"available": [
52
],
"BN": [
53
],
"packages.": [
54
],
"CVNetica": [
55,
129
],
"open-source,": [
57
],
"written": [
58
],
"in": [
59,
126
],
"Python,": [
60
],
"extends": [
62
],
"the": [
63
],
"Netica": [
64
],
"package": [
66
],
"perform": [
68
],
"cross-validation": [
69,
81
],
"read,": [
71
],
"rebuild,": [
72
],
"learn": [
74
],
"data.": [
77
],
"Insights": [
78
],
"gained": [
79
],
"implications": [
83
],
"on": [
84,
144
],
"prediction": [
85,
127
],
"versus": [
86
],
"description": [
87
],
"illustrated": [
89
],
"with:": [
90
],
"data-driven": [
92
],
"oceanographic": [
93
],
"application;": [
94
],
"model-emulation": [
97
],
"application.": [
98
],
"These": [
99
],
"examples": [
100
],
"show": [
101
],
"that": [
102
],
"occurs": [
104
],
"when": [
105
],
"become": [
107
],
"more": [
108
],
"than": [
110
],
"allowed": [
111
],
"by": [
112
],
"supporting": [
113
],
"data": [
114
],
"incurs": [
117
],
"computational": [
118
],
"costs": [
119
],
"as": [
120,
122
],
"well": [
121
],
"causing": [
123
],
"reduction": [
125
],
"evaluates": [
130
],
"using": [
132
],
"several": [
133
],
"complexity": [
134
],
"metrics": [
135,
146
],
"(we": [
136,
147
],
"used": [
137,
148
],
"level": [
138
],
"discretization)": [
140
],
"its": [
142
],
"impact": [
143
],
"performance": [
145
],
"skill).": [
149
]
},
"apc_list": {
"value": 3400,
"currency": "USD",
"value_usd": 3400,
"provenance": "doaj"
},
"apc_paid": null,
"authorships": [
{
"author_position": "first",
"author": {
"id": "https://openalex.org/A5081887696",
"display_name": "Michael N. Fienen",
"orcid": "https://orcid.org/0000-0002-7756-4651"
},
"institutions": [
{
"id": "https://openalex.org/I1286329397",
"display_name": "United States Geological Survey",
"ror": "https://ror.org/035a68863",
"country_code": "US",
"type": "government",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
]
}
],
"countries": [
"US"
],
"is_corresponding": false,
"raw_author_name": "Michael N. Fienen",
"raw_affiliation_strings": [
"US Geological Survey, Wisconsin Water Science Center, 8505 Research Way, Middleton, WI 53562, USA."
],
"affiliations": [
{
"raw_affiliation_string": "US Geological Survey, Wisconsin Water Science Center, 8505 Research Way, Middleton, WI 53562, USA.",
"institution_ids": [
"https://openalex.org/I1286329397"
]
}
]
},
{
"author_position": "last",
"author": {
"id": "https://openalex.org/A5065618772",
"display_name": "Nathaniel G. Plant",
"orcid": "https://orcid.org/0000-0002-5703-5672"
},
"institutions": [
{
"id": "https://openalex.org/I1286329397",
"display_name": "United States Geological Survey",
"ror": "https://ror.org/035a68863",
"country_code": "US",
"type": "government",
"lineage": [
"https://openalex.org/I1286329397",
"https://openalex.org/I1335927249"
]
}
],
"countries": [
"US"
],
"is_corresponding": false,
"raw_author_name": "Nathaniel G. Plant",
"raw_affiliation_strings": [
"US Geological Survey, St. Petersburg Coastal and Marine Science Center, 600 Fourth Street South, St. Petersburg, FL 33701, USA#TAB#"
],
"affiliations": [
{
"raw_affiliation_string": "US Geological Survey, St. Petersburg Coastal and Marine Science Center, 600 Fourth Street South, St. Petersburg, FL 33701, USA#TAB#",
"institution_ids": [
"https://openalex.org/I1286329397"
]
}
]
}
],
"best_oa_location": null,
"biblio": {
"volume": "63",
"issue": null,
"first_page": "14",
"last_page": "23"
},
"citation_normalized_percentile": {
"value": 0.999968,
"is_in_top_1_percent": true,
"is_in_top_10_percent": true
},
"cited_by_api_url": "https://api.openalex.org/works?filter=cites:W1983416467",
"cited_by_count": 47,
"cited_by_percentile_year": {
"min": 96,
"max": 97
},
"concepts": [
{
"id": "https://openalex.org/C22019652",
"wikidata": "https://www.wikidata.org/wiki/Q331309",
"display_name": "Overfitting",
"level": 3,
"score": 0.9711819
},
{
"id": "https://openalex.org/C519991488",
"wikidata": "https://www.wikidata.org/wiki/Q28865",
"display_name": "Python (programming language)",
"level": 2,
"score": 0.73592293
},
{
"id": "https://openalex.org/C41008148",
"wikidata": "https://www.wikidata.org/wiki/Q21198",
"display_name": "Computer science",
"level": 0,
"score": 0.7342191
},
{
"id": "https://openalex.org/C119857082",
"wikidata": "https://www.wikidata.org/wiki/Q2539",
"display_name": "Machine learning",
"level": 1,
"score": 0.6903952
},
{
"id": "https://openalex.org/C27181475",
"wikidata": "https://www.wikidata.org/wiki/Q541014",
"display_name": "Cross-validation",
"level": 2,
"score": 0.6095968
},
{
"id": "https://openalex.org/C149810388",
"wikidata": "https://www.wikidata.org/wiki/Q5374873",
"display_name": "Emulation",
"level": 2,
"score": 0.6009559
},
{
"id": "https://openalex.org/C154945302",
"wikidata": "https://www.wikidata.org/wiki/Q11660",
"display_name": "Artificial intelligence",
"level": 1,
"score": 0.5956933
},
{
"id": "https://openalex.org/C124101348",
"wikidata": "https://www.wikidata.org/wiki/Q172491",
"display_name": "Data mining",
"level": 1,
"score": 0.49534097
},
{
"id": "https://openalex.org/C2777904410",
"wikidata": "https://www.wikidata.org/wiki/Q7397",
"display_name": "Software",
"level": 2,
"score": 0.45933902
},
{
"id": "https://openalex.org/C107673813",
"wikidata": "https://www.wikidata.org/wiki/Q812534",
"display_name": "Bayesian probability",
"level": 2,
"score": 0.42313325
},
{
"id": "https://openalex.org/C98045186",
"wikidata": "https://www.wikidata.org/wiki/Q205663",
"display_name": "Process (computing)",
"level": 2,
"score": 0.4208276
},
{
"id": "https://openalex.org/C50644808",
"wikidata": "https://www.wikidata.org/wiki/Q192776",
"display_name": "Artificial neural network",
"level": 2,
"score": 0.12101033
},
{
"id": "https://openalex.org/C199360897",
"wikidata": "https://www.wikidata.org/wiki/Q9143",
"display_name": "Programming language",
"level": 1,
"score": 0.11138287
},
{
"id": "https://openalex.org/C162324750",
"wikidata": "https://www.wikidata.org/wiki/Q8134",
"display_name": "Economics",
"level": 0,
"score": 0.0
},
{
"id": "https://openalex.org/C50522688",
"wikidata": "https://www.wikidata.org/wiki/Q189833",
"display_name": "Economic growth",
"level": 1,
"score": 0.0
}
],
"corresponding_author_ids": [],
"corresponding_institution_ids": [],
"countries_distinct_count": 1,
"counts_by_year": [
{
"year": 2024,
"cited_by_count": 2
},
{
"year": 2023,
"cited_by_count": 4
},
{
"year": 2022,
"cited_by_count": 4
},
{
"year": 2021,
"cited_by_count": 7
},
{
"year": 2020,
"cited_by_count": 2
},
{
"year": 2019,
"cited_by_count": 7
},
{
"year": 2018,
"cited_by_count": 7
},
{
"year": 2017,
"cited_by_count": 3
},
{
"year": 2016,
"cited_by_count": 3
},
{
"year": 2015,
"cited_by_count": 5
}
],
"created_date": "2016-06-24",
"datasets": [],
"display_name": "A cross-validation package driving Netica with python",
"doi": "https://doi.org/10.1016/j.envsoft.2014.09.007",
"fulltext_origin": "ngrams",
"fwci": 6.013,
"grants": [],
"has_fulltext": true,
"id": "https://openalex.org/W1983416467",
"ids": {
"openalex": "https://openalex.org/W1983416467",
"doi": "https://doi.org/10.1016/j.envsoft.2014.09.007",
"mag": "1983416467"
},
"indexed_in": [
"crossref"
],
"institutions_distinct_count": 1,
"is_paratext": false,
"is_retracted": false,
"keywords": [
{
"id": "https://openalex.org/keywords/overfitting",
"display_name": "Overfitting",
"score": 0.9711819
},
{
"id": "https://openalex.org/keywords/python",
"display_name": "Python (programming language)",
"score": 0.73592293
},
{
"id": "https://openalex.org/keywords/cross-validation",
"display_name": "Cross-validation",
"score": 0.6095968
},
{
"id": "https://openalex.org/keywords/optimization",
"display_name": "Optimization",
"score": 0.504492
}
],
"language": "en",
"locations": [
{
"is_oa": false,
"landing_page_url": "https://doi.org/10.1016/j.envsoft.2014.09.007",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S113611870",
"display_name": "Environmental Modelling & Software",
"issn_l": "1364-8152",
"issn": [
"1364-8152",
"1873-6726"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": null,
"license_id": null,
"version": null,
"is_accepted": false,
"is_published": false
}
],
"locations_count": 1,
"mesh": [],
"ngrams_url": "https://api.openalex.org/works/W1983416467/ngrams",
"open_access": {
"is_oa": false,
"oa_status": "closed",
"oa_url": null,
"any_repository_has_fulltext": false
},
"primary_location": {
"is_oa": false,
"landing_page_url": "https://doi.org/10.1016/j.envsoft.2014.09.007",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S113611870",
"display_name": "Environmental Modelling & Software",
"issn_l": "1364-8152",
"issn": [
"1364-8152",
"1873-6726"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": true,
"host_organization": "https://openalex.org/P4310320990",
"host_organization_name": "Elsevier BV",
"host_organization_lineage": [
"https://openalex.org/P4310320990"
],
"host_organization_lineage_names": [
"Elsevier BV"
],
"type": "journal"
},
"license": null,
"license_id": null,
"version": null,
"is_accepted": false,
"is_published": false
},
"primary_topic": {
"id": "https://openalex.org/T11303",
"display_name": "Learning and Inference in Bayesian Networks",
"score": 0.9979,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
"publication_date": "2015-01-01",
"publication_year": 2015,
"referenced_works": [
"https://openalex.org/W141376853",
"https://openalex.org/W1488679267",
"https://openalex.org/W1509562192",
"https://openalex.org/W1532221965",
"https://openalex.org/W1541190072",
"https://openalex.org/W1554944419",
"https://openalex.org/W1598201578",
"https://openalex.org/W1693305809",
"https://openalex.org/W1755360231",
"https://openalex.org/W1883185126",
"https://openalex.org/W1981532962",
"https://openalex.org/W1989020770",
"https://openalex.org/W2032907044",
"https://openalex.org/W2033904036",
"https://openalex.org/W2044327045",
"https://openalex.org/W2048076161",
"https://openalex.org/W2049633694",
"https://openalex.org/W2079087023",
"https://openalex.org/W2083743686",
"https://openalex.org/W2098487338",
"https://openalex.org/W2130089609",
"https://openalex.org/W2145691535",
"https://openalex.org/W2155826788",
"https://openalex.org/W2163134152",
"https://openalex.org/W2242464395",
"https://openalex.org/W2256314547",
"https://openalex.org/W2545363345",
"https://openalex.org/W2787894218",
"https://openalex.org/W3005347330",
"https://openalex.org/W4236619250",
"https://openalex.org/W4299670631"
],
"referenced_works_count": 31,
"related_works": [
"https://openalex.org/W4378510483",
"https://openalex.org/W4362597605",
"https://openalex.org/W4297676672",
"https://openalex.org/W4281702477",
"https://openalex.org/W2922073769",
"https://openalex.org/W2801469686",
"https://openalex.org/W2149903055",
"https://openalex.org/W2149651625",
"https://openalex.org/W1983416467",
"https://openalex.org/W1574414179"
],
"sustainable_development_goals": [
{
"score": 0.44,
"id": "https://metadata.un.org/sdg/4",
"display_name": "Quality education"
}
],
"title": "A cross-validation package driving Netica with python",
"topics": [
{
"id": "https://openalex.org/T11303",
"display_name": "Learning and Inference in Bayesian Networks",
"score": 0.9979,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11801",
"display_name": "Advanced Techniques in Reservoir Management",
"score": 0.9838,
"subfield": {
"id": "https://openalex.org/subfields/2212",
"display_name": "Ocean Engineering"
},
"field": {
"id": "https://openalex.org/fields/22",
"display_name": "Engineering"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
{
"id": "https://openalex.org/T11986",
"display_name": "Management and Reproducibility of Scientific Workflows",
"score": 0.9525,
"subfield": {
"id": "https://openalex.org/subfields/1802",
"display_name": "Information Systems and Management"
},
"field": {
"id": "https://openalex.org/fields/18",
"display_name": "Decision Sciences"
},
"domain": {
"id": "https://openalex.org/domains/2",
"display_name": "Social Sciences"
}
}
],
"type": "article",
"type_crossref": "journal-article",
"updated_date": "2024-08-15T01:47:23.658016",
"versions": []
}
}
