Neuro-Fuzzy Decision Tree by Fuzzy ID3 Algorithm and Its Application to Anti-Dumping Early-Warning System

2006 ◽  
Author(s):  
Jianna Zhao ◽  
Zhipeng Chang
Keyword(s):  
Decision Tree ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Id3 Algorithm ◽  
Neuro Fuzzy

Induction of fuzzy decision trees and its refinement using gradient projected-neuro-fuzzy decision tree

2014 ◽  
Vol 6 (4) ◽  
pp. 346 ◽  
Author(s):  
Swathi Jamjala Narayanan ◽  
Rajen B. Bhatt ◽  
Ilango Paramasivam ◽  
M. Khalid ◽  
B.K. Tripathy
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Neuro Fuzzy ◽  
Fuzzy Decision Trees

scholarly journals Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gang Wang ◽  
Keming Wang ◽  
Yingying Zhou ◽  
Xiaoyan Mo ◽  
Weilin Xiao
Keyword(s):  
Financial Crisis ◽  
Decision Tree ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Predictive Ability ◽  
Tree Models ◽  
The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

scholarly journals Early Warning System Untuk Prediksi Tingkat Pelayanan Jalan di Jalur SSA Kota Bogor

KREA-TIF ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 9
Author(s):  
Fitria Rachmawati ◽  
Dahlia Widhyaestoeti
Keyword(s):  
Early Warning ◽  
Fuzzy Inference System ◽  
Early Warning System ◽  
Fuzzy Inference ◽  
Warning System ◽  
Inference System ◽  
Neuro Fuzzy

<p><em>Kepadatan lalu lintas yang cukup tinggi pada akhir pekan dan hari kerja dikarenakan kota Bogor merupakan kota tujuan wisata serta penunjang kegiatan di DKI Jakarta, sehingga</em><em> menyebabkan over capacity. Penerapan jalur SSA dilakukan sebagai upaya untuk mengurangi tingkat kemacetan yang terjadi pada jalur tersebut. Maksud dari penelitian ini adalah untuk membuat suatu model prediksi yang dikembangkan dalam sebuah sistem aplikasi yang bisa digunakan untuk mendekteksi kemacetan terutama di ruas sistem satu arah kota Bogor. Pengumpulan data dilakukan beberapa proses diantaranya melakukan survey dan pengamatan lalu lintas di jalur SSA pada Dinas Perhubungan Kota Bogor. Prediksi kemacetan arus lalu lintas menggunakan metode ANFIS (Adaptive Neuro Fuzzy Inference System). Hasil prediksi ANFIS kemudian digunakan untuk mengukur tingkat pelayanan jalan berdasarkan karakteristik arus lalu lintas yang ditandai dalam suatu nilai rasio perbandingan antara volume kendaraan dan kapasitas jalan. Pada hasil prediksi yang sudah dilakukan diketahui jumlah kendaraan yang melewati jalus SSA mencapai 5034 dengan kapasitas jalan 6400. Sehingga status kemacetan yang terjadi berada di level C dengan nilai 0,78. Dimana tingkat Pelayanan pada nilai rasio tersebut memiliki karakteristik arus stabil tetapi pergerakan kendaraan dikendalikan oleh volume lalu lintas yang lebih tinggi dengan kecepatan sebesar 60 Km/Jam dan kepadatan lalu lintas sedang</em>.<em></em></p>

SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG KORONER DENGAN METODE PROBABILISTIC FUZZY DECISION TREE

2018 ◽  
Vol 4 (2) ◽  
pp. 106
Author(s):  
Wizra Aulia
Keyword(s):  
Blood Pressure ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Chest Pain ◽  
Expert System ◽  
Decision Tree ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Id3 Algorithm ◽  
Anticipatory Action

<p><em>Di Indonesia, penyakit jantung koroner menempati posisi pertama sebagai penyakit yang paling banyak mengakibatkan kematian. Jika gejala penyakit jantung koroner  dikenali sejak dini maka dapat dilakukan tindakan antisipasi. Diagnosa dilakukan berdasarkan 6 gejala penyakit jantung koroner yaitu sakit dada, tekanan darah tinggi, kolesterol, kadar gula darah, hasil EKG dan jumlah denjut jantung. Metode yang dipakai adalah Probabilistic Fuzzy Decision Tree (PFDT) dengan algoritma  Probabilistic Fuzzy  ID3. Hasil keakuratan sistem pakar diagnosa penyakit jantung koroner dengan metode PFDT mencapai 95%.</em><em></em></p><p><em>In Indonesia, coronary heart disease the first position as the disease that most resulted in death. If symptoms of coronary heart disease are recognized early on, anticipatory action may be taken. Diagnosis is based on 6 symptoms of coronary heart disease  chest pain, high blood pressure, cholesterol, blood sugar, ECG results and </em>heartbeat<em>. The method used is Probabilistic Fuzzy Decision Tree (PFDT) with Probabilistic Fuzzy ID3 algorithm. The result of accuracy of expert system of diagnosis of coronary heart disease by PFDT method reached 95%.</em></p>

scholarly journals Fuzzy Decision Tree to Predict Student Success in Their Studies

2020 ◽  
Vol 1 (3) ◽  
pp. 135-144
Author(s):  
Heri Bambang Santoso
Keyword(s):  
Decision Tree ◽  
Fuzzy Inference ◽  
Drop Out ◽  
Classification Rules ◽  
University Management ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Inference System ◽  
Target Time ◽  
Id3 Algorithm

The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

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