Decision Tree Divide and Conquer Classification

2018 ◽  
pp. 307-343 ◽  
Author(s):  
Ivo D. Dinov
Keyword(s):  
Decision Tree ◽  
Divide And Conquer

scholarly journals Machine Learning by Data Mining REPTree and M5P for Predicating Novel Information for PM10

2020 ◽  
pp. 40-48
Author(s):  
Yas Alsultanny
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Meteorological Parameters ◽  
Pm10 Concentration ◽  
Divide And Conquer ◽  
Time Processing ◽  
Elapsed Time ◽  
Tree Algorithms

We examined data mining as a technique to extract knowledge from database to predicate PM10 concentration related to meteorological parameters. The purpose of this paper is to compare between the two types of machine learning by data mining decision tree algorithms Reduced Error Pruning Tree (REPTree) and divide and conquer M5P to predicate Particular Matter 10 (PM10) concentration depending on meteorological parameters. The results of the analysis showed M5P tree gave higher correlation compared with REPTree, moreover lower errors, and higher number of rules, the elapsed time for processing REPTree is less than the time processing of M5P. Both of these trees proved that humidity absorbed PM10. The paper recommends REPTree and M5P for predicting PM10 and other pollution gases.

Emergence of Learning Rule in Neural Networks Using Genetic Programming Combined with Decision Trees

1999 ◽  
Vol 3 (4) ◽  
pp. 223-233 ◽  
Author(s):  
Noboru Matsumoto ◽  
◽  
Kenneth J. Mackin ◽  
Eiichiro Tazaki
Keyword(s):  
Neural Networks ◽  
Decision Tree ◽  
Genetic Programming ◽  
Decision Trees ◽  
Back Propagation ◽  
Learning Rule ◽  
Divide And Conquer ◽  
Effective Learning ◽  
Learning Rules ◽  
Initial Network

Genetic Programming (GP) combined with Decision Trees is used to evolve the structure and weights for Artificial Neural Networks (ANN). The learning rule of the decision tree is defined as a function of global information using a divide-and-conquer strategy. Learning rules with lower fitness values are replaced by new ones generated by GP techniques. The reciprocal connection between decision tree and GP emerges from the coordination of learning rules. Since there is no constraint on initial network, a more suitable network is found for a given task. Fitness values are improved using a Hybrid GP technique combining GP and Back Propagation. The proposed method is applied to medical diagnosis and results demonstrate that effective learning rules evolve.

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

KLASIFIKASI SELEKSI ATRIBUT PADA SERANGAN SPAM MENGGUNAKAN METODE ALGORITMA DECISION TREE

2018 ◽  
Vol 14 (2) ◽  
pp. 145
Author(s):  
Aji Sudibyo ◽  
Taufik Asra ◽  
Bakhtiar Rifai
Keyword(s):  
Decision Tree

internet sangat biasa untuk sekarang ini, penggunaaan internetnya tak lepas dari penggunaan email, salah satu ancaman yang terjadi ketika menggunakan email adalah spam, spam  merupakan pesan atau email yang tidak diinginkan oleh penerimanya dan dikirimkan secara massa.        Penelitian tentang serangan spam didapat dari dataset spam sebanyak 4601 record yang terdiri 1813 record dianggap spam dan 278 data bukan spam dengan atribut awal sebanyak 57 atribute dengan 1 atribute class, pada ekperimen yang dilakukan menggunakan select attribute dengan decision tree menjadi 15 atribute dengan 1 atribute class dilakukan 3 percobaan pengujian dengan persentase atribute 30%, 50% dan 70% select atribute didapat hasil fitur select atribute sebesar 70% didapat hasil lebih baik dari 30% ataupun 50% dengan nilai accuracy sebesar 92.469%.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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