scholarly journals Applying particle swarm optimization-based decision tree classifier for wart treatment selection

2021 ◽  
Author(s):  
Junhua Hu ◽  
Xiangzhu Ou ◽  
Pei Liang ◽  
Bo Li
Keyword(s):  
Decision Tree ◽  
Particle Swarm ◽  
Classification And Regression Tree ◽  
Particle Swarm Algorithm ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Cart Algorithm ◽  
Better Than

AbstractWart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.

scholarly journals Random Forest: A Hybrid Implementation for Sarcasm Detection in Public Opinion Mining

2019 ◽  
Vol 8 (12) ◽  
pp. 5022-5025
Keyword(s):  
Decision Making ◽  
Public Opinion ◽  
Random Forest ◽  
Decision Tree ◽  
Opinion Mining ◽  
Random Forest Classifier ◽  
Decision Tree Classifier ◽  
Wrong Decision ◽  
Tree Classifier ◽  
Better Than

Modelling the sentiment with context is one of the most important part in Sentiment analysis. There are various classifiers which helps in detecting and classifying it. Detection of sentiment with consideration of sarcasm would make it more accurate. But detection of sarcasm in people review is a challenging task and it may lead to wrong decision making or classification if not detected. This paper uses Decision Tree and Random forest classifiers and compares the performance of both. Here we consider the random forest as hybrid decision tree classifier. We propose that performance of random forest classifier is better than any other normal decision tree classifier with appropriate reasoning

scholarly journals A Boosted Decision Tree Model for Predicting Loan Default in P2P Lending Communities

2019 ◽  
Vol 9 (1) ◽  
pp. 1257-1261
Keyword(s):  
Small Business ◽  
Decision Tree ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Loan Default ◽  
Accuracy Profile ◽  
Default Prediction ◽  
Tree Classifier ◽  
Social Lending ◽  
Boosted Decision Tree

Loan Default Prediction For Social Lending Is An Emerging Area Of Research In Predictive Analytics. The Need For Large Amount Of Data And Few Available Studies In The Current Loan Default Prediction Models For Social Lending Suggest That Other Viable And Easily Implementable Models Should Be Investigated And Developed. In View Of This, This Study Developed A Data Mining Model For Predicting Loan Default Among Social Lending Patrons, Specifically The Small Business Owners, Using Boosted Decision Tree Model. The United States Small Business Administration (Usba) PubliclyAvailable Loan Administration Dataset Of 27 Features And 899164 Data Instances Was Used In 80:20 Ratios For The Training And Testing Of The Model. 16 Data Features Were Finally Used As Predictors After Data Cleaning And Feature Engineering. The Gradient Boosting Decision Tree Classifier Recorded 99% Accuracy Compared To The Basic Decision Tree Classifier Of 98%. The Model Is Further Evaluated With (A) Receiver Operating Characteristics (Roc) And Area Under Curve (Auc), (B) Cumulative Accuracy Profile (Cap), And (C) Cumulative Accuracy Profile (Cap) Under Auc. Each Of These Model Performance Evaluation Metrics, Especially Roc-Auc, Showed The Relationship Between The True Positives And False Positives That Implies The Model Is A Good Fit.

scholarly journals Knowledge discovery from gene expression dataset using bagging lasso decision tree

2021 ◽  
Vol 21 (2) ◽  
pp. 1151
Author(s):  
Umu Sa'adah ◽  
Masithoh Yessi Rochayani ◽  
Ani Budi Astuti
Keyword(s):  
Gene Expression ◽  
Decision Tree ◽  
Gene Expression Data ◽  
High Dimensional Data ◽  
Decision Tree Model ◽  
High Dimensional ◽  
Expression Data ◽  
Tree Model ◽  
Tree Classifier ◽  
Cart Algorithm

<p>Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.</p>

scholarly journals Design Design the temperature and humidity classification of the workspace by using a decision tree model.

2020 ◽  
Vol 6 (2) ◽  
pp. 169-178
Author(s):  
Wahyu Setiady ◽  
Y.B. Adyapaka Apatya
Keyword(s):  
Decision Tree ◽  
Decision Tree Model ◽  
Raspberry Pi ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Tree Classifier ◽  
Temperature And Humidity

Rancang bangun alat klasifikasi suhu dan kelembaban ruang kerja dengan menggunakan model decision tree. Berdasarkan tabel standar tata cara perencanaan teknis konservasi energi pada bangunan gedung, suhu nyaman optimal ada pada kisaran 22,8oC – 25,8 oC dengan ambang atas 28 oC dan kelembaban 70%. Dengan memanfaatkan decision tree classifier, suhu dan kelembaban ruangan yang dideteksi oleh sensor DHT11 diklasifikasikan berdasarkan model yang telah dibuat dengan menggunakan Raspberry Pi 3 dan node red. Penelitian ini dilaksanakan di laboratorium komputer Politeknik Industri ATMI yang juga digunakan sebagai laboratorium riset terapan yang bekerjasama dengan industri dalam bidang pengembangan perangkat lunak otomasi. Penelitian ini berhasil membuat alat klasifikasi suhu dan kelembaban ruang kerja dengan menggunakan model decision tree yang menghasilkan status dingin, sejuk nyaman, nyaman optimal, hangat nyaman dan panas dengan tingkat prediksi model 0,983.  

FALL DETECTION USING THREE WEARABLE TRIAXIAL ACCELEROMETERS AND A DECISION-TREE CLASSIFIER

2014 ◽  
Vol 26 (05) ◽  
pp. 1450059 ◽  
Author(s):  
Kan Luo ◽  
Jianqing Li ◽  
Jianfeng Wu ◽  
Hua Yang ◽  
Gaozhi Xu
Keyword(s):  
Decision Tree ◽  
Detection System ◽  
Human Movement ◽  
Fall Detection ◽  
The Body ◽  
Body Tilt ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Tree Classifier ◽  
Unintentional Falls

Unintentional falls cause serious health problem and high medical cost, particularly among the elders. Efficient fall detection can ensure fallen subjects with timely rescue, less pain and lower health-care expense. However, the accuracy of the present fall detection system with single accelerometer does not meet the requirement of practical application. In this paper, a fall detection method using three wearable triaxial accelerometers and a decision-tree classifier is proposed. The three triaxial accelerometers are, respectively mounted on the head, the waist and the ankle to capture the acceleration signals of human movement. A Kalman filter is adopted to estimate the body tilt angle. After the features are extracted, the trained decision-tree model is used to predict the fall. The efficiency improvement is evidenced by the scripted and unscripted lateral fall experiments, involving five young healthy volunteers (three males and two females; age: 23.3 ± 1 years). The classification of fall and activities of daily living (ADL) achieve recall, precision and F-value of 93.1%, 95.9%, and 94.5%, respectively, and the system detects all falls during the extended unscripted trials. The experimental results indicate that the complementary movement information coming from three accelerometers can enhance the performance of fall detection. The proposed method is efficient, and it has remarkable improvements in comparison to the method of using one or two accelerometers.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION (EMD) FOR AUTOMATED DETECTION OF EPILEPSY USING EEG SIGNALS

2012 ◽  
Vol 22 (06) ◽  
pp. 1250027 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
U. RAJENDRA ACHARYA ◽  
JEN HONG TAN ◽  
ANDREA PETZNICK ◽  
RATNA YANTI ◽  
...  
Keyword(s):  
Decision Tree ◽  
Empirical Mode Decomposition ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Spectral Energy ◽  
Intrinsic Mode Functions ◽  
Decision Tree Classifier ◽  
Mode Decomposition ◽  
Tree Classifier ◽  
Highly Nonlinear

Epilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.

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