Predicting criminal recidivism using neural networks
Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a s...
Gespeichert in:
| Veröffentlicht in: | Socio-economic planning sciences 2000-12, Vol.34 (4), p.271-284 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 284 |
|---|---|
| container_issue | 4 |
| container_start_page | 271 |
| container_title | Socio-economic planning sciences |
| container_volume | 34 |
| creator | Palocsay, Susan W. Wang, Ping Brookshire, Robert G. |
| description | Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain. |
| doi_str_mv | 10.1016/S0038-0121(00)00003-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_38922148</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0038012100000033</els_id><sourcerecordid>38922148</sourcerecordid><originalsourceid>FETCH-LOGICAL-c283m-f6e70750b03688f9216f3d5bffe678dd46a236b084b8c8bd93acb9445af0ef3d3</originalsourceid><addsrcrecordid>eNqFkVtLxDAQhYMouK7-BGGfRB-qk0vT9Elk8cqCgvoc2nSq0e3FpF3Zf2-6lX3dwGRg8p1DckLIKYVLClRevQJwFQFl9BzgAsLiEd8jE6oSHkkQdJ9MtsghOfL-KzBMsHhCxIvDwprO1h8z42xl62w5c2hsYVfWV7PeDyc19i7Ma-x-G_ftj8lBmS09nvz3KXm_u32bP0SL5_vH-c0iMkzxKiolJpDEkAOXSpUpo7LkRZyXJcpEFYWQGeMyByVyZVRepDwzeSpEnJWAgeRTcjb6tq756dF3urLe4HKZ1dj0XnOVMkaF2gnSJOES5ADGI2hc473DUrfh0Zlbawp6CFNvwtRDUhpAb8LUPOieRp3DFs1WhIi-Mdh6vdI84yJs61AsqEKzoYZRO4wSqpkS-rOrgtn1aIYhu5VFp72xWJvwESH5TheN3XGdP9nnlGo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>17736068</pqid></control><display><type>article</type><title>Predicting criminal recidivism using neural networks</title><source>RePEc</source><source>ScienceDirect Journals (5 years ago - present)</source><creator>Palocsay, Susan W. ; Wang, Ping ; Brookshire, Robert G.</creator><creatorcontrib>Palocsay, Susan W. ; Wang, Ping ; Brookshire, Robert G.</creatorcontrib><description>Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.</description><identifier>ISSN: 0038-0121</identifier><identifier>EISSN: 1873-6041</identifier><identifier>DOI: 10.1016/S0038-0121(00)00003-3</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Criminology ; forecasting ; Mathematical analysis ; neural networks ; North Carolina ; Offenders ; prediction ; Probability ; Recidivism ; Regression analysis ; Social research ; Statistical models ; U.S.A</subject><ispartof>Socio-economic planning sciences, 2000-12, Vol.34 (4), p.271-284</ispartof><rights>2000 Elsevier Science Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c283m-f6e70750b03688f9216f3d5bffe678dd46a236b084b8c8bd93acb9445af0ef3d3</citedby><cites>FETCH-LOGICAL-c283m-f6e70750b03688f9216f3d5bffe678dd46a236b084b8c8bd93acb9445af0ef3d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S0038-0121(00)00003-3$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,778,782,3539,3996,27911,27912,45982</link.rule.ids><backlink>$$Uhttp://econpapers.repec.org/article/eeesoceps/v_3a34_3ay_3a2000_3ai_3a4_3ap_3a271-284.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Palocsay, Susan W.</creatorcontrib><creatorcontrib>Wang, Ping</creatorcontrib><creatorcontrib>Brookshire, Robert G.</creatorcontrib><title>Predicting criminal recidivism using neural networks</title><title>Socio-economic planning sciences</title><description>Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.</description><subject>Criminology</subject><subject>forecasting</subject><subject>Mathematical analysis</subject><subject>neural networks</subject><subject>North Carolina</subject><subject>Offenders</subject><subject>prediction</subject><subject>Probability</subject><subject>Recidivism</subject><subject>Regression analysis</subject><subject>Social research</subject><subject>Statistical models</subject><subject>U.S.A</subject><issn>0038-0121</issn><issn>1873-6041</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqFkVtLxDAQhYMouK7-BGGfRB-qk0vT9Elk8cqCgvoc2nSq0e3FpF3Zf2-6lX3dwGRg8p1DckLIKYVLClRevQJwFQFl9BzgAsLiEd8jE6oSHkkQdJ9MtsghOfL-KzBMsHhCxIvDwprO1h8z42xl62w5c2hsYVfWV7PeDyc19i7Ma-x-G_ftj8lBmS09nvz3KXm_u32bP0SL5_vH-c0iMkzxKiolJpDEkAOXSpUpo7LkRZyXJcpEFYWQGeMyByVyZVRepDwzeSpEnJWAgeRTcjb6tq756dF3urLe4HKZ1dj0XnOVMkaF2gnSJOES5ADGI2hc473DUrfh0Zlbawp6CFNvwtRDUhpAb8LUPOieRp3DFs1WhIi-Mdh6vdI84yJs61AsqEKzoYZRO4wSqpkS-rOrgtn1aIYhu5VFp72xWJvwESH5TheN3XGdP9nnlGo</recordid><startdate>20001201</startdate><enddate>20001201</enddate><creator>Palocsay, Susan W.</creator><creator>Wang, Ping</creator><creator>Brookshire, Robert G.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U1</scope><scope>7U2</scope><scope>C1K</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20001201</creationdate><title>Predicting criminal recidivism using neural networks</title><author>Palocsay, Susan W. ; Wang, Ping ; Brookshire, Robert G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c283m-f6e70750b03688f9216f3d5bffe678dd46a236b084b8c8bd93acb9445af0ef3d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Criminology</topic><topic>forecasting</topic><topic>Mathematical analysis</topic><topic>neural networks</topic><topic>North Carolina</topic><topic>Offenders</topic><topic>prediction</topic><topic>Probability</topic><topic>Recidivism</topic><topic>Regression analysis</topic><topic>Social research</topic><topic>Statistical models</topic><topic>U.S.A</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Palocsay, Susan W.</creatorcontrib><creatorcontrib>Wang, Ping</creatorcontrib><creatorcontrib>Brookshire, Robert G.</creatorcontrib><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Socio-economic planning sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Palocsay, Susan W.</au><au>Wang, Ping</au><au>Brookshire, Robert G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting criminal recidivism using neural networks</atitle><jtitle>Socio-economic planning sciences</jtitle><date>2000-12-01</date><risdate>2000</risdate><volume>34</volume><issue>4</issue><spage>271</spage><epage>284</epage><pages>271-284</pages><issn>0038-0121</issn><eissn>1873-6041</eissn><abstract>Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/S0038-0121(00)00003-3</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0038-0121 |
| ispartof | Socio-economic planning sciences, 2000-12, Vol.34 (4), p.271-284 |
| issn | 0038-0121 1873-6041 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_38922148 |
| source | RePEc; ScienceDirect Journals (5 years ago - present) |
| subjects | Criminology forecasting Mathematical analysis neural networks North Carolina Offenders prediction Probability Recidivism Regression analysis Social research Statistical models U.S.A |
| title | Predicting criminal recidivism using neural networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T02%3A04%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Predicting%20criminal%20recidivism%20using%20neural%20networks&rft.jtitle=Socio-economic%20planning%20sciences&rft.au=Palocsay,%20Susan%20W.&rft.date=2000-12-01&rft.volume=34&rft.issue=4&rft.spage=271&rft.epage=284&rft.pages=271-284&rft.issn=0038-0121&rft.eissn=1873-6041&rft_id=info:doi/10.1016/S0038-0121(00)00003-3&rft_dat=%3Cproquest_cross%3E38922148%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=17736068&rft_id=info:pmid/&rft_els_id=S0038012100000033&rfr_iscdi=true |