Relevant bibliographies by topics / Gini impurity

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gini impurity'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Gini impurity"

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Yuan, Ye, Liji Wu, and Xiangmin Zhang. "Gini-Impurity Index Analysis." IEEE Transactions on Information Forensics and Security 16 (2021): 3154–69. http://dx.doi.org/10.1109/tifs.2021.3076932.

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Singh, Sudhir Kuamr, and Dr Vipin Saxena. "Reducing the Impurity of Object-Oriented DatabaseThrough Gini Index." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 11 (November 30, 2014): 5172–78. http://dx.doi.org/10.24297/ijct.v13i11.2787.

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In the current scenario, the size of database is increasing due to audio and video files. In the database, irregularities occur due to duplication of data at many places, therefore, it needs reconstruction of database size. The present work deals with reducing of impurity through a well-known Gini index technique. Since many of software’s are using the object-oriented databases, therefore, an object-oriented database is considered, A real object-oriented database for Electricity Bill Deposit System is considered. A sample size of 15 records is considered, however the present technique can be applied for large size or even for the complex database. A decision tree is constructed and sample queries are performed for verifying the result and Gini index is computed for minimizing the impurity in the presented object-oriented database. Â
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Singh, Vishwa Pratap, and R. L. Ujjwal. "Gini impurity based NDN cache pollution attack defence mechanism." Journal of Information and Optimization Sciences 41, no. 6 (August 17, 2020): 1353–63. http://dx.doi.org/10.1080/02522667.2020.1809092.

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Jiang, Longquan, Bo Zhang, Qin Ni, Xuan Sun, and Pingping Dong. "Prediction of SNP Sequences via Gini Impurity Based Gradient Boosting Method." IEEE Access 7 (2019): 12647–57. http://dx.doi.org/10.1109/access.2019.2893269.

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Zhi, Ting, Hongbin Luo, and Ying Liu. "A Gini Impurity-Based Interest Flooding Attack Defence Mechanism in NDN." IEEE Communications Letters 22, no. 3 (March 2018): 538–41. http://dx.doi.org/10.1109/lcomm.2018.2789896.

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Kwon, Taeyong, and Sanghoo Yoon. "Design of rain gauge network using entropy and Gini impurity: A case study of Gangwon Province." Journal of the Korean Data And Information Science Society 31, no. 4 (July 31, 2020): 569–77. http://dx.doi.org/10.7465/jkdi.2020.31.4.569.

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Laber, Eduardo, and Lucas Murtinho. "Minimization of Gini Impurity: NP-completeness and Approximation Algorithm via Connections with the k-means Problem." Electronic Notes in Theoretical Computer Science 346 (August 2019): 567–76. http://dx.doi.org/10.1016/j.entcs.2019.08.050.

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Grabmeier, Johannes L., and Larry A. Lambe. "Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson's chi-square test." International Journal of Business Intelligence and Data Mining 2, no. 2 (2007): 213. http://dx.doi.org/10.1504/ijbidm.2007.013938.

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Jia, Naizheng, Dian Zu, and Baojun Jia. "Influence Analysis in Music: Artists and Genres." Journal of Innovation and Social Science Research 8, no. 7 (July 30, 2021): 199–203. http://dx.doi.org/10.53469/jissr.2021.08(07).36.

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Music is an important part of human civilization, which is accompanied by the emergence and development of human civilization. While music is developing, different genres of music or musicians’ styles are all influencing each other. Therefore, it is significant to understand and measure the influence of music influencers and their followers in different genres of music with social development. Address to identify the influence network of American music, firstly, a PageRank model was proposed to calculate the music influence of every artist among the network. According to the results, we built the subnetworks to illustrate the influence of different sets. Then, we did research on distinguishing between genres, which utilized the random forest classifier. Furthermore, Gini impurity is used to demonstrate the feature's importance. Our result shows that Pop is the best influential genre, and acousticness is the most distinguishing feature.
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Qu, Hongquan, Zhanli Fan, Shuqin Cao, Liping Pang, Hao Wang, and Jie Zhang. "A Study on Sensitive Bands of EEG Data under Different Mental Workloads." Algorithms 12, no. 7 (July 22, 2019): 145. http://dx.doi.org/10.3390/a12070145.

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Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.
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Dissertations / Theses on the topic "Gini impurity"

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Blank, Clas, and Tomas Hermansson. "A Machine Learning approach to churn prediction in a subscription-based service." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240397.

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Prenumerationstjänster blir alltmer populära i dagens samhälle. En av nycklarna för att lyckas med en prenumerationsbaserad affärsmodell är att minimera kundbortfall (eng. churn), dvs. kunder som avslutar sin prenumeration inom en viss tidsperiod. I och med den ökande digitaliseringen, är det nu enklare att samla in data än någonsin tidigare. Samtidigt växer maskininlärning snabbt och blir alltmer lättillgängligt, vilket möjliggör nya infallsvinklar på problemlösning. Denna rapport kommer testa och utvärdera ett försök att förutsäga kundbortfall med hjälp av maskininlärning, baserat på kunddata från ett företag med en prenumerationsbaserad affärsmodell där prenumeranten får besöka live-event till en fast månadskostnad. De maskininlärningsmodeller som användes i testerna var Random Forests, Support Vector Machines, Logistic Regression, och Neural Networks som alla tränades med användardata från företaget. Modellerna gav ett slutligt träffsäkerhetsresultat i spannet mellan 73,7 % och 76,7 %. Därutöver tenderade modellerna att ge ett högre resultat för precision och täckning gällande att klassificera kunder som sagt upp sin prenumeration än för de som fortfarande var aktiva. Dessutom kunde det konstateras att de kundegenskaper som hade störst inverkan på klassifikationen var ”Använda Biljetter” och ”Längd på Prenumeration”. Slutligen kommer det i denna rapport diskuteras hur informationen angående vilka kunder som sannolikt kommer avsluta sin prenumeration kan användas ur ett mer affärsmässigt perspektiv.
In today’s world subscription-based online services are becoming increasingly popular. One of the keys to success in a subscription-based business model is to minimize churn, i.e. customer canceling their subscriptions. Due to the digitalization of the world, data is easier to collect than ever before. At the same time machine learning is growing and is made more available. That opens up new possibilities to solve different problems with the use of machine learning. This paper will test and evaluate a machine learning approach to churn prediction, based on the user data from a company with an online subscription service letting the user attend live shows to a fixed price. To perform the tests different machine learning models were used, both individually and combined. The models were Random Forests, Support Vector Machines, Logistic Regression and Neural Networks. In order to train them a data set containing either active or churned users was provided. Eventually the models returned accuracy results ranging from 73.7 % to 76.7 % when classifying churners based on their activity data. Furthermore, the models turned out to have higher scores for precision and recall for classifying the churners than the non-churners. In addition, the features that had the most impact on the model regarding the classification were Tickets Used and Length of Subscription. Moreover, this paper will discuss how churn prediction can be used from a business perspective.
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Ekeberg, Lukas, and Alexander Fahnehjelm. "Maskininlärning som verktyg för att extrahera information om attribut kring bostadsannonser i syfte att maximera försäljningspris." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240401.

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The Swedish real estate market has been digitalized over the past decade with the current practice being to post your real estate advertisement online. A question that has arisen is how a seller can optimize their public listing to maximize the selling premium. This paper analyzes the use of three machine learning methods to solve this problem: Linear Regression, Decision Tree Regressor and Random Forest Regressor. The aim is to retrieve information regarding how certain attributes contribute to the premium value. The dataset used contains apartments sold within the years of 2014-2018 in the Östermalm / Djurgården district in Stockholm, Sweden. The resulting models returned an R2-value of approx. 0.26 and Mean Absolute Error of approx. 0.06. While the models were not accurate regarding prediction of premium, information was still able to be extracted from the models. In conclusion, a high amount of views and a publication made in April provide the best conditions for an advertisement to reach a high selling premium. The seller should try to keep the amount of days since publication lower than 15.5 days and avoid publishing on a Tuesday.
Den svenska bostadsmarknaden har blivit alltmer digitaliserad under det senaste årtiondet med nuvarande praxis att säljaren publicerar sin bostadsannons online. En fråga som uppstår är hur en säljare kan optimera sin annons för att maximera budpremie. Denna studie analyserar tre maskininlärningsmetoder för att lösa detta problem: Linear Regression, Decision Tree Regressor och Random Forest Regressor. Syftet är att utvinna information om de signifikanta attribut som påverkar budpremien. Det dataset som använts innehåller lägenheter som såldes under åren 2014-2018 i Stockholmsområdet Östermalm / Djurgården. Modellerna som togs fram uppnådde ett R²-värde på approximativt 0.26 och Mean Absolute Error på approximativt 0.06. Signifikant information kunde extraheras from modellerna trots att de inte var exakta i att förutspå budpremien. Sammanfattningsvis skapar ett stort antal visningar och en publicering i april de bästa förutsättningarna för att uppnå en hög budpremie. Säljaren ska försöka hålla antal dagar sedan publicering under 15.5 dagar och undvika att publicera på tisdagar.
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Hansén, Jacob, and Axel Gustafsson. "A Study on Comparison Websites in the Airline Industry and Using CART Methods to Determine Key Parameters in Flight Search Conversion." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254309.

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This bachelor thesis in applied mathematics and industrial engineering and management aimed to identify relationships between search parameters in flight comparison search engines and the exit conversion rate, while also investigating how the emergence of such comparison search engines has impacted the airline industry. To identify such relationships, several classification models were employed in conjunction with several sampling methods to produce a predictive model using the program R. To investigate the impact of the emergence of comparison websites, Porter's 5 forces and a SWOT - analysis were employed to analyze findings of a literature study and a qualitative interview. The classification models developed performed poorly with regards to several assessments metrics which suggested that there were little to no significance in the relationship between the search parameters investigated and exit conversion rate. Porter's 5 forces and the SWOT-analysis suggested that the competitive landscape of the airline industry has become more competitive and that airlines which do not manage to adapt to this changing market environment will experience decreasing profitability.
Detta kandidatexamensarbete inriktat på tillämpad matematik och industriell ekonomi syftade till att identifiera samband mellan sökparametrar från flygsökmotorer och konverteringsgraden för utträde till ett flygbolags hemsida, och samtidigt undersöka hur uppkomsten av flygsökmotorer har påverkat flygindustrin för flygbolag. För att identifiera sådana samband, tillämpades flera klassificeringsmodeller tillsammans med stickprovsmetoder för att bygga en predikativ modell i programmet R. För att undersöka påverkan av flygsökmotorer tillämpades Porters 5 krafter och SWOT-analys som teoretiska ramverk för att analysera information uppsamlad genom en litteraturstudie och en intervju. Klassificeringsmodellerna som byggdes presterade undermåligt med avseende på flera utvärderingsmått, vilket antydde att det fanns lite eller inget samband mellan de undersökta sökparametrarna och konverteringsgraden för utträde. Porters 5 krafter och SWOT-analysen visade att flygindustrin hade blivit mer konkurrensutsatt och att flygbolag som inte lyckas anpassa sig efter en omgivning i ändring kommer att uppleva minskande lönsamhet.
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Book chapters on the topic "Gini impurity"

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D’Ambrosio, Antonio, and Valerio A. Tutore. "Conditional Classification Trees by Weighting the Gini Impurity Measure." In Studies in Classification, Data Analysis, and Knowledge Organization, 273–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11363-5_31.

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Conference papers on the topic "Gini impurity"

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Ch'ng, Chee Keong, and Nor Idayu Mahat. "Winsorised gini impurity: A resistant to outliers splitting metric for classification tree." In INTERNATIONAL CONFERENCE ON QUANTITATIVE SCIENCES AND ITS APPLICATIONS (ICOQSIA 2014): Proceedings of the 3rd International Conference on Quantitative Sciences and Its Applications. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4903661.

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Das, Saptarshi, Shamik Sural, Jaideep Vaidya, and Vijayalakshmi Atluri. "Using Gini Impurity to Mine Attribute-based Access Control Policies with Environment Attributes." In SACMAT '18: The 23rd ACM Symposium on Access Control Models and Technologies. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205977.3208949.

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Tan, Junyuan, Shan Jing, Lei Guo, and Bin Xiao. "DDoS detection method based on Gini impurity and random forest in SDN environment." In 2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC）. IEEE, 2021. http://dx.doi.org/10.1109/spac53836.2021.9539920.

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Yin, Hao, Zhenpo Wang, Peng Liu, Zhaosheng Zhang, and Yang Li. "Voltage Fault Diagnosis of Power Batteries based on Boxplots and Gini Impurity for Electric Vehicles." In 2019 Electric Vehicles International Conference (EV). IEEE, 2019. http://dx.doi.org/10.1109/ev.2019.8892849.

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