scholarly journals Quantum-Like Sampling

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2036
Author(s):  
Andreas Wichert
Keyword(s):  
Probability Theory ◽  
Linear Relation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Degrees Of Belief ◽  
Bayes Classifier ◽  
Data Set ◽  
Degree Of Belief ◽  
The Mean ◽  
Training Sets

Probability theory is built around Kolmogorov’s axioms. To each event, a numerical degree of belief between 0 and 1 is assigned, which provides a way of summarizing the uncertainty. Kolmogorov’s probabilities of events are added, the sum of all possible events is one. The numerical degrees of belief can be estimated from a sample by its true fraction. The frequency of an event in a sample is counted and normalized resulting in a linear relation. We introduce quantum-like sampling. The resulting Kolmogorov’s probabilities are in a sigmoid relation. The sigmoid relation offers a better importability since it induces the bell-shaped distribution, it leads also to less uncertainty when computing the Shannon’s entropy. Additionally, we conducted 100 empirical experiments by quantum-like sampling 100 times a random training sets and validation sets out of the Titanic data set using the Naïve Bayes classifier. In the mean the accuracy increased from 78.84% to 79.46%.

scholarly journals PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET

2017 ◽  
Vol 5 (8) ◽  
pp. 260-266
Author(s):  
Subhankar Manna ◽  
Malathi G.
Keyword(s):  
Decision Tree ◽  
Metabolic Diseases ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set ◽  
Id3 Algorithm

Healthcare industry collects huge amount of unclassified data every day.  For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using R package. This work used the dataset that is imported from the UCI Machine Learning Repository, Diabetes 130-US hospitals for years 1999-2008 Data Set. Motivation/Background: Naïve Bayes is a probabilistic classifier based on Bayes theorem. It provides useful perception for understanding many algorithms. In this paper when Bayesian algorithm applied on diabetes dataset, it shows high accuracy. Is assumes variables are independent of each other. In this paper, we construct a decision tree from diabetes dataset in which it selects attributes at each other node of the tree like graph and model, each branch represents an outcome of the test, and each node hold a class attribute. This technique separates observation into branches to construct tree. In this technique tree is split in a recursive way called recursive partitioning. Decision tree is widely used in various areas because it is good enough for dataset distribution. For example, by using ID3 (Decision tree) algorithm we get a result like they are belong to diabetes or not. Method: We will use Naïve Bayes for probabilistic classification and ID3 for decision tree.  Results: The dataset is related to Diabetes dataset. There are 18 columns like – Races, Gender, Take_metformin, Take_repaglinide, Insulin, Body_mass_index, Self_reported_health etc. and 623 rows. Naive Bayes Classifier algorithm will be used for getting the probability of having diabetes or not. Here Diabetes is the class for Diabetes data set. There are two conditions “Yes” and “No” and have some personal information about the patient like - Races, Gender, Take_metformin, Take_repaglinide, Insulin, Body_mass_index, Self_reported_health etc. We will see the probability that for “Yes” what unit of probability and for “No” what unit of probability which is given bellow. For Example: Gender – Female have 0.4964 for “No” and 0.5581 for “Yes” and for Male 0.5035 is for “No” and 0.4418 for “Yes”. Conclusions: In this paper two algorithms had been implemented Naive Bayes Classifier algorithm and ID3 algorithm. From Naive Bayes Classifier algorithm, the probability of having diabetes has been predicted and from ID3 algorithm a decision tree has been generated.

scholarly journals Optimasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Anomaly dengan Univariate Fitur Selection

2020 ◽  
Vol 4 (2) ◽  
pp. 40-49
Author(s):  
Harianto Harianto ◽  
◽  
Andi Sunyoto ◽  
Sudarmawan Sudarmawan ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Data Set ◽  
System Data ◽  
And Training

System and network security from interference from parties who do not have access to the system is the most important in a system. To realize a system, data or network that is safe at unauthorized users or other interference, a system is needed to detect it. Intrusion-Detection System (IDS) is a method that can be used to detect suspicious activity in a system or network. The classification algorithm in artificial intelligence can be applied to this problem. There are many classification algorithms that can be used, one of which is Naïve Bayes. This study aims to optimize Naïve Bayes using Univariate Selection on the UNSW-NB 15 data set. The features used only take 40 features that have the best relevance. Then the data set is divided into two test data and training data, namely 10%: 90%, 20%: 70%, 30%: 70%, 40%: 60% and 50%: 50%. From the experiments carried out, it was found that feature selection had quite an effect on the accuracy value obtained. The highest accuracy value is obtained when the data set is divided into 40%: 60% for both feature selection and non-feature selection. Naïve Bayes with unselected features obtained the highest accuracy value of 91.43%, while with feature selection 91.62%, using feature selection could increase the accuracy value by 0.19%.

scholarly journals A Comparative Approach of Dimensionality Reduction Techniques in Text Classification

2019 ◽  
Vol 9 (6) ◽  
pp. 4974-4979 ◽  
Author(s):  
S. Rahamat Basha ◽  
J. K. Rani
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Comparative Approach ◽  
Bayes Classifier ◽  
Average Precision ◽  
Data Set ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Document Categorization ◽  
Feature Selection Techniques

This work deals with document classification. It is a supervised learning method (it needs a labeled document set for training and a test set of documents to be classified). The procedure of document categorization includes a sequence of steps consisting of text preprocessing, feature extraction, and classification. In this work, a self-made data set was used to train the classifiers in every experiment. This work compares the accuracy, average precision, precision, and recall with or without combinations of some feature selection techniques and two classifiers (KNN and Naive Bayes). The results concluded that the Naive Bayes classifier performed better in many situations.

scholarly journals Feature Scaled Element Balancing with Random Boosting for Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 4105-4110
Keyword(s):  
Principal Component Analysis ◽  
Heart Disease ◽  
Naive Bayes ◽  
Principal Component ◽  
Component Analysis ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set

In the current scenario, the researchers are focusing towards health care project for the prediction of the disease and its type. In addition to the prediction, there exists a need to find the influencing parameter that directly related to the disease prediction. The analysis of the parameters needed to the prediction of the disease still remains a challenging issue. With this view, we focus on predicting the heart disease by applying the dataset with boosting the parameters of the dataset. The heart disease data set extracted from UCI Machine Learning Repository is used for implementation. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the attribute relationship is identified by the correlation values. Second, the data set is fitted to random boost regressor and the important features are identified. Third, the dataset is feature scaled reduced and then fitted to random forest classifier, decision tree classifier, Naïve bayes classifier, logistic regression classifier, kernel support vector machine and KNN classifier. Fourth, the dataset is reduced with principal component analysis with five components and then fitted to the above mentioned classifiers. Fifth, the performance of the classifiers is analyzed with the metrics like accuracy, recall, fscore and precision. Experimental results shows that, the Naïve bayes classifier is more effective with the precision, Recall and Fscore of 0.89 without random boost, 0.88 with random boosting and 0.90 with principal component analysis. Experimental results show, the Naïve bayes classifier is more effective with the accuracy of 89% without random boost, 90% with random boosting and 91% with principal component analysis.

Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung

2020 ◽  
Vol 4 (2) ◽  
pp. 437 ◽  
Author(s):  
Dito Putro Utomo ◽  
Mesran Mesran
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Diagnostic Process ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set ◽  
Pca Method

Heart disease is a disease with a high mortality rate, there are 12 million deaths each year worldwide. This is what causes the need for early diagnosis to find out the heart disease. But the process of diagnosis is quite challenging because of the complex relationship between the attributes of heart disease. So it is important to know the main attributes that are used as a decision making process or the classification process in heart disease. In this study the dataset used has 57 types of attributes in it. So that reduction is needed to shorten the diagnostic process, the reduction process can be carried out using the Principal Component Analysis (PCA) method. The PCA method itself can be combined with data mining calcification techniques to measure the accuracy of the dataset. This study compares the accuracy rate using the C5.0 algorithm and the Naïve Bayes Classifier (NBC) algorithm, the results obtained both after and before the reduction are Naïve Bayes Classifier (NBC) algorithms that have better performance than the C5.0 algorithm

METODE ALGORITMA NAIVE BAYES CLASSIFIER DALAM MEMPREDIKSI JADWAL BERLAYAR ANGKUTAN LAUT (FERY) BULUKUMBA KEPULAUAN SELAYAR

2019 ◽  
Vol 13 (01) ◽  
pp. 1886-1891
Author(s):  
Rizal Syarifuddin ◽  
Rosmiati Rosmiati
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set

Kecelakaan laut yang mengakibatkan musibah tenggelamnya kapal laut angkutan barang dan orang diakibatkan salah satunya adalah faktor cuaca. Akses akan informasi perkiraan cuaca menjadi penting sebelum kapten kapal laut memutuskan untuk melakukan pelayaran. Oleh karena itu, penelitian ini bertujuan melakukan penghitungan menggunakan algoritma naïve bayes dalam membantu kapten kapal mengambil keputusan untuk berlayar atau tidak. Penelitian ini dilakukan pada kapal roro penyeberangan laut dari pelabuhan bira Kabupaten Bulukumba ke Pelabuhan Benteng Kepulauan Selayar. Kriteria atau atribut yang digunakan untuk mengklasifikasi diperoleh dari data badan meterologi dan geofisika terkait parameter cuaca seperti angina didaratan dan buih gelombang laut sebagai atribut. Hasil pengujian penghitungan menunjukkan bahwa data set tersebut dapat diimplementasikan pada penghitungan algorithma naïve bayes untuk dipakai mengambil keputusan untuk melakukan pelayaran.

scholarly journals Deteksi Konten Gereflekter pada Cerita Anak Menggunakan Naïve Bayes Classifier

2020 ◽  
Vol 4 (2) ◽  
pp. 318
Author(s):  
Mayya Tania Wewengkang ◽  
Dana Sulistiyo Kusumo ◽  
Widi Astuti
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
School Level ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Test Scenario ◽  
Data Set ◽  
Positive Class ◽  
Negative Class

Textbooks and storybooks are the ones used as a source of knowledge. When children read a book, they will try to interpret each word and sentence in it. However, it will be a problem if the book contains vulgar words and indecent sentences. For children at the elementary school level, it is not allowed. For this research, we called that content as gereflekter content. Based on these problems, this research was conducted by building a system to detect gereflekter content in the text of the child's stories that were used as a data set. A system is built by using Naïve Bayes Classifier (NBC) and then evaluated in two scenarios using accuracy, precision, and recall metrics because the characteristics of the data set are imbalanced with the amount of data in the negative class are greater than the data in the positive class. From evaluation results, test scenario produced a high average precision of 99.01%, whereas the recall value has an average of above 50%. From these two values, it can be concluded that the model built by the system has not detected the class properly, but highly trusted when it does.

scholarly journals Attribute Balanced Leveling with Ada Boost Regressor for Predicting Heart Disease using Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 2488-2493
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Principal Component ◽  
Naïve Bayes ◽  
Experimental Results ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Data Set ◽  
Customer Churn

The technological advancement can help the entire application field to predict the damage and to forecast the future target of the object. The wealth of the world is in the health of the people. So the technology must support the technologists in predicting the disease in advance. The machine learning is the emerging field which is used to forecast the existence of the heart disease through the values of the clinical parameters. With this view, we focus on predicting the customer churn for the banking application. This paper uses the customer churn bank modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data is processed to find the relationship between the elements of the dataset. Second, the data set is applied for Ada Boost regressors and the important elements are identified. Third, the dataset is applied to feature scaling and then fitted to kernel support vector machine, logistic regression classifier, Naive bayes classifier, random forest classifier, decision tree classifier and KNN classifier. Fourth, the dataset is dimensionality reduced with principal component analysis with five components and then applied to the previously mentioned classifiers. Fifth, the performance of the classifiers is analyzed with the indication metrics like precision, accuracy, recall and Fscore. The implementation is carried out with python code using Anaconda Navigator. Experimental results show that, the Naïve bayes classifier is more effective with the precision of 0.90 for dataset with random boost, feature scaled and PCA. Experimental results show that, the Naïve bayes classifier is more effective with the recall of 0.91 for dataset with random boost, feature scaled and PCA. Experimental results show that, the Naïve bayes classifier is more effective with the Fscore of 0.92 for dataset with random boost, feature scaled and PCA. Experimental results show, the Naïve bayes classifier is more effective with the accuracy of 91% without random boost, 93% with random boosting and 92% with principal component analysis.

Prediksi Harga Emas dengan Menggunakan Metode Naïve Bayes dalam Investasi untuk Meminimalisasi Resiko

2018 ◽  
Vol 2 (1) ◽  
pp. 354-360
Author(s):  
Mohammad Guntur ◽  
Julius Santony ◽  
Yuhandri Yuhandri
Keyword(s):  
Naive Bayes ◽  
Supply And Demand ◽  
Naïve Bayes ◽  
Decision Makers ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The Mean ◽  
Bayes Algorithm

The high low price of gold influenced by many factors such as economic conditions, inflation rate, supply and demand and much more. The Naïve Bayes algorithm is capable of generating a classification that is used to predict future opportunities. By using the Naïve Bayes Classifier algorithm obtained a prediction of gold prices that can help decision makers in determining whether to sell or buy gold. By using the Naïve Bayes Classifier algorithm obtained a prediction of gold prices that can help decision makers in determining whether to sell or buy gold. Gold data will be processed using Rapidminer software. Stages of processing are reading training data, calculating the mean and standard deviation, entering the test data and finding the density value of gauss and then looking for probability value. Based on the calculation that has been done, Naïve Bayes Classifier method is able to predict the price of gold for 1 day ahead or every day. With the results of this calculation is expected to help gold investment actors in increasing accuracy to predict gold prices for decision making.

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