Relevant bibliographies by topics / C4.5 algorithm

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Academic literature on the topic 'C4.5 algorithm'

Author: Grafiati
Published: 3 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "C4.5 algorithm"

1

Phalak, Priti, and Rekha Sharma. "Optimization of Horizontal Aggregation in SQL by using C4. 5 Algorithm." International Journal of Computer Applications 93, no. 13 (2014): 31–37. http://dx.doi.org/10.5120/16277-6048.

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2

Sharma, Seema, Jitendra Agrawal, and Sanjeev Sharma. "Classification Through Machine Learning Technique: C4. 5 Algorithm based on Various Entropies." International Journal of Computer Applications 82, no. 16 (2013): 28–32. http://dx.doi.org/10.5120/14249-2444.

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3

Redemann, J., Q. Zhang, J. Livingston, et al. "Testing aerosol properties in MODIS Collection 4 and 5 using airborne sunphotometer observations in INTEX-B/MILAGRO." Atmospheric Chemistry and Physics 9, no. 21 (2009): 8159–72. http://dx.doi.org/10.5194/acp-9-8159-2009.

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Abstract. The 14-channel Ames Airborne Tracking Sunphotometer (AATS) was operated on a Jetstream 31 (J31) aircraft in March 2006 during MILAGRO/INTEX-B (Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment). We compare AATS retrievals of spectral aerosol optical depth (AOD) and related aerosol properties with corresponding spatially coincident and temporally near-coincident measurements acquired by the MODIS-Aqua and MODIS-Terra satellite sensors. These comparisons are carried out for the older MODIS Collection 4 (C4) and the new Collection 5 (C5) data set, the latter representing a reprocessing of the entire MODIS data set completed during 2006 with updated calibration and aerosol retrieval algorithm. Our analysis yields a direct, validated assessment of the differences between select MODIS C4 and C5 aerosol retrievals. Our analyses of 37 coincident observations by AATS and MODIS-Terra and 18 coincident observations between AATS and MODIS-Aqua indicate notable differences between MODIS C4 and C5 and between the two sensors. For MODIS-Terra, we find an average increase in AOD of 0.02 at 553 nm and 0.01 or less at the shortwave infrared (SWIR) wavelengths. The change from C4 to C5 results in less good agreement with the AATS derived spectral AOD, with average differences at 553 nm increasing from 0.03 to 0.05. For MODIS-Aqua, we find an average increase in AOD of 0.008 at 553 nm, but an increase of nearly 0.02 at the SWIR wavelengths. The change from C4 to C5 results in slightly less good agreement to the AATS derived visible AOD, with average differences at 553 nm increasing from 0.03 to 0.04. However, at SWIR wavelengths, the changes from C4 to C5 result in improved agreement between MODIS-Aqua and AATS, with the average differences at 2119 nm decreasing from −0.02 to −0.003. Comparing the Angstrom exponents calculated from AOD at 553nm and 855nm, we find an increased rms difference from AATS derived Angstrom exponents in going from C4 to C5 for MODIS-Terra, and a decrease in rms difference, hence an improvement, for the transition from C4 to C5 in MODIS-Aqua. Combining the AATS retrievals with in situ measurements of size-dependent aerosol extinction, we derive a suborbital measure of the aerosol submicron fraction (SMF) of AOD and compare it to MODIS retrievals of aerosol fine mode fraction (FMF). Our analysis shows a significant rms-difference between the MODIS-Terra FMF and suborbitally-derived SMF of 0.17 for both C4 and C5. For MODIS-Aqua, there is a slight improvement in the transition from C4 to C5, with the rms-difference from AATS dropping from 0.23 to 0.16. The differences in MODIS C4 and C5 AOD in this limited data set can be traced to changes in the reflectances input to the aerosol retrievals. An extension of the C4-C5 comparisons from the area along the J31 flight track to a larger study region between 18–23° N and 93–100° W on each of the J31 flight days supports the finding of significant differences between MODIS C4 and C5.
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4

Redemann, J., Q. Zhang, J. Livingston, et al. "Testing aerosol properties in MODIS (MOD04/MYD04) Collection 4 and 5 using airborne sunphotometer observations in INTEX-B/MILAGRO." Atmospheric Chemistry and Physics Discussions 9, no. 3 (2009): 11753–81. http://dx.doi.org/10.5194/acpd-9-11753-2009.

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Abstract. The 14-channel Ames Airborne Tracking Sunphotometer (AATS) was operated on a Jetstream 31 (J31) aircraft in March 2006 during MILAGRO/INTEX-B (Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment). We compare AATS retrievals of spectral aerosol optical depth (AOD) and related aerosol properties with corresponding spatially coincident and temporally near-coincident measurements acquired by the MODIS-Aqua and MODIS-Terra satellite sensors. These comparisons are carried out for the older MODIS Collection 4 (C4) and the new Collection 5 (C5) data set, the latter representing a reprocessing of the entire MODIS data set completed during 2006 with updated calibration and aerosol retrieval algorithm. Our analysis yields a direct, validated assessment of the differences between select MODIS C4 and C5 aerosol retrievals. Our analyses of 37 coincident observations by AATS and MODIS-Terra and 18 coincident observations between AATS and MODIS-Aqua indicate notable differences between MODIS C4 and C5 and between the two sensors. For MODIS-Terra, we find an average increase in AOD of 0.02 at 553 nm and 0.01 or less at the shortwave infrared (SWIR) wavelengths. The change from C4 to C5 results in less good agreement with the AATS derived spectral AOD, with average differences at 553 nm increasing from 0.03 to 0.05. For MODIS-Aqua, we find an average increase in AOD of 0.008 at 553 nm, but an increase of nearly 0.02 at the SWIR wavelengths. The change from C4 to C5 results in slightly less good agreement to the AATS derived visible AOD, with average differences at 553 nm increasing from 0.03 to 0.04. However, at SWIR wavelengths, the changes from C4 to C5 result in improved agreement between MODIS-Aqua and AATS, with the average differences at 2119 nm decreasing from -0.02 to -0.003. Comparing the Angstrom exponents calculated from AOD at 553 nm and 855 nm, we find an increased rms difference from AATS derived Angstrom exponents in going from C4 to C5 for MODIS-Terra, and a decrease in rms difference, hence an improvement, for the transition from C4 to C5 in MODIS-Aqua. Combining the AATS retrievals with in situ measurements of size-dependent aerosol extinction, we derive a suborbital measure of the aerosol submicron fraction (SMF) of AOD and compare it to MODIS retrievals of aerosol fine mode fraction (FMF). Our analysis shows a significant rms-difference between the MODIS-Terra FMF and suborbitally-derived SMF of 0.17 for both C4 and C5. For MODIS-Aqua, there is a slight improvement in the transition from C4 to C5, with the rms-difference from AATS dropping from 0.23 to 0.16. The differences in MODIS C4 and C5 AOD in this limited data set can be traced to changes in the reflectances input to the aerosol retrievals. An extension of the C4-C5 comparisons from the area along the J31 flight track to a larger study region between 18–23° N and 93–100° W on each of the J31 flight days supports the finding of significant differences between MODIS C4 and C5.
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5

Suherman, Asep, DIDI KURNAEDI, Sofian Lusa, and Rizqi Darmawan. "Junior Class Preparedness Classification Faces A National Exam Using C.45 Algorithm with A Particle Swarm Optimization Approach." bit-Tech 2, no. 3 (2020): 101–9. http://dx.doi.org/10.32877/bt.v2i3.133.

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These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage.
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6

Siregar, Kristian. "TESTING THE C4.5 ALGORITHM WITH RAPID MINER TO DETERMINE DECISIONS FOR IMPLEMENTING SPORTS ACTIVITIES." INFOKUM 11, no. 04 (2023): 40–47. http://dx.doi.org/10.58471/infokum.v11i04.1790.

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In the current era of information technology, decision making is an important aspect in various fields, including in the world of sports. The decision to carry out sports activities can be influenced by many factors such as weather conditions, availability of facilities, and the physical condition of the participants. With advances in technology and data science, there are algorithms that can assist in making these decisions. One of them is the C4.5 algorithm. This study aims to test the effectiveness of the C4.5 algorithm in determining decisions to carry out sports activities using the Rapid Miner software. The data used in this study is historical data from sports activities which include variables such as weather, date, and condition of the participants. The test results show that the C4. 5 is able to provide decisions with fairly high accuracy. By using Rapid Miner software, the process of learning and testing data becomes faster and more efficient. The conclusion of this study is that the C4.5 algorithm, with the help of Rapid Miner software, can be used as an effective method to assist in making decisions about sports activities.
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7

Suherman, Asep, Didi Kurnaedi, and Rizqi Darmawan. "Junior Class Preparedness Classification Faces A National Exam Using A C.45 Algorithm With A Particle Swarm Optimization Approach." bit-Tech 3, no. 1 (2020): 11–20. http://dx.doi.org/10.32877/bt.v3i1.169.

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These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage
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8

Santoso, Trio, and Fitria Saftarina. "Clusterization Of Paddy Field Farmers Condition In Kota Metro Lampung Province Indonesia Using K-Means Clustering Algorithm." Journal of Agribusiness and Community Empowerment 3, no. 1 (2020): 37–43. http://dx.doi.org/10.32530/jace.v3i1.187.

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Metro City is one of the administrative cities in Lampung province, Indonesia and also one of important rice producing regions in Lampung. Application of intensive agriculture in trend of declining area, low quality of land and differences of farmers internal characteristics that requires special treatment as solutions. Clustering farmers using the K-Means Cluster algorithm and Elbow Method can be used to facilitate policy makers determine programs and activities must be taken. Results showed that farmers are ideally grouped into 5 clusters (C1, C2, C3, C4 and C5). C1 members having most family members (4,54 persons). C2 members are the oldest age (68 years old) with longest farming experience (52.00 years) but have lowest formal education (7.67 years), least family members (3.33 person) and lowest total area (0.37 hectare). C3 having highest formal education (14.60 years) and largest paddy fields (0.80 hectare) but don't use any pesticides in paddy cropping management. Whereas farmers in C4 have largest family members helped (2.00 people). Farmers in C5 are the youngest (45.50 years old) and having the shortest experience (29.50 years) but use the most types (4 brands) and amounts of pesticides (400.00 mm.hectare.rotation-1) in paddy field management practices in Metro City.
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9

Renaldi, Renaldi, and Yusuf Kurnia. "Alleged Bad Credit at Saving Cooperatives Borrow Flamboyant Assistance PPSW Jakarta With Comparasion the Algorithms Naive Bayes and C4.5." bit-Tech 2, no. 3 (2020): 141–47. http://dx.doi.org/10.32877/bt.v2i3.163.

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Data mining is often used in the financial sector, one of which is cooperatives. According to Law No. 25 of 1992, what is meant by cooperatives are business entities whose members are individual or cooperative legal entities based on activities based on the principles of cooperatives as well as as a people's economic movement based on the principle of kinship. One of the things that needs to be considered is the provision of credit or borrowing in the Flamboyan cooperative, which in this study there are many bad crediting occurrences that occur in the Flamboyan cooperative. By using a lot of data mining techniques, data can be utilized optimally. From the above problems, it can be overcome by utilizing data mining techniques, namely Predicting Bad Credit at the Flamboyant Savings and Loan Cooperative Fostered by PPSW Jakarta Using Comparative Algorithms Naive Bayes and C4.5. The algorithm used in the system is the best result of the Naive Bayes and C4.5 comparison based on data from the Flamboyan cooperative. The results obtained from the comparative data processing between the Naïve Bayes algorithm and the C4.5 using a dataset of 2282 transaction data obtained the results of the accuracy of the Naïve Bayes algorithm of 69.19% and the C4.5 algorithm of 71.87%, based on the accuracy results state that the C4 algorithm .5 is superior to the Naïve Bayes algorithm. Then the results from the C4.5 decision tree are translated into the bad credit prediction system in the Flamboyan cooperative.
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10

Sugianto, Castaka Agus, and Nadya Selvi Pujiyanita. "OPTIMALISASI ALGORITMA C4.5 MENGGUNAKAN ALGORITMA GENETIKA UNTUK PREDIKSI KELULUSAN SISWA SMKN 2 CIMAHI." Infotekmesin 10, no. 2 (2019): 9–14. http://dx.doi.org/10.35970/infotekmesin.v10i2.35.

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SMK 2 Cimahi is an educational institution Vocational high school in Cimahi. Vocational high schools play an important role in creating the best graduates for the needs of the workforce. The graduation process is the latest activity of student management. Student graduation criteria for national exams must meet minimum scores, complete all subjects, take school exams, national exams, and competency tests. In the 2015 class of 300 students, students who did not pass the national exam were 1 student, in the 2016 class of 299 students, students who did not pass the national exam were 1 student, whereas in the 2017 class totaling 302 students 100% students graduate on time. However in the coming year, it is still unknown whether students will be 100% graduated or not. Therefore this study was conducted to predict student graduation using the C4.5 algorithm + Genetic Algorithm. C4.5 algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall and 1 second execution time. While the C4.5 + genetic algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall AUC 0.500, and 36 second execution time and after T-test testing between the C4.5 Algorithm and C4 Algorithm .5 +, Genetic Algorithm is said to be significant if alpha = 0.050. The results of this study obtained alpha = 1,000.
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Books on the topic "C4.5 algorithm"

1

Quinlan, J. Ross. C4. 5: Programs for Machine Learning. Elsevier Science & Technology Books, 2014.

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Book chapters on the topic "C4.5 algorithm"

1

Lin, Shaorong. "Based on C4 Design and Implementation of College Health Sports System Based on 5 Algorithm." In Application of Big Data, Blockchain, and Internet of Things for Education Informatization. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23947-2_56.

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Conference papers on the topic "C4.5 algorithm"

1

Shen, Yanguang, Hui Shao, and Jianzhong Huang. "Research on Privacy Preserving Distributed C4. 5 Algorithm." In 2009 3rd International Symposium on Intelligent Information Technology Application Workshops (IITAW). IEEE, 2009. http://dx.doi.org/10.1109/iitaw.2009.81.

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