scholarly journals F*: an interpretable transformation of the F-measure

2021 ◽  
Vol 110 (3) ◽  
pp. 451-456
Author(s):  
David J. Hand ◽  
Peter Christen ◽  
Nishadi Kirielle
Keyword(s):  
Harmonic Mean ◽  
Classification Algorithms ◽  
Simple Transformation ◽  
Intuitive Interpretation ◽  
F Measure

AbstractThe F-measure, also known as the F1-score, is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call $$F^*$$ F ∗ (F-star), which has an immediate practical interpretation.

scholarly journals Early Detection of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier), Infestation Using Data Mining

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 95
Author(s):  
Heba Kurdi ◽  
Amal Al-Aldawsari ◽  
Isra Al-Turaiki ◽  
Abdulrahman S. Aldawood
Keyword(s):  
Data Mining ◽  
Plant Size ◽  
Support Vector ◽  
Classification Algorithms ◽  
Palm Tree ◽  
Rhynchophorus Ferrugineus ◽  
Red Palm Weevil ◽  
Palm Weevil ◽  
Using Data ◽  
F Measure

In the past 30 years, the red palm weevil (RPW), Rhynchophorus ferrugineus (Olivier), a pest that is highly destructive to all types of palms, has rapidly spread worldwide. However, detecting infestation with the RPW is highly challenging because symptoms are not visible until the death of the palm tree is inevitable. In addition, the use of automated RPW weevil identification tools to predict infestation is complicated by a lack of RPW datasets. In this study, we assessed the capability of 10 state-of-the-art data mining classification algorithms, Naive Bayes (NB), KSTAR, AdaBoost, bagging, PART, J48 Decision tree, multilayer perceptron (MLP), support vector machine (SVM), random forest, and logistic regression, to use plant-size and temperature measurements collected from individual trees to predict RPW infestation in its early stages before significant damage is caused to the tree. The performance of the classification algorithms was evaluated in terms of accuracy, precision, recall, and F-measure using a real RPW dataset. The experimental results showed that infestations with RPW can be predicted with an accuracy up to 93%, precision above 87%, recall equals 100%, and F-measure greater than 93% using data mining. Additionally, we found that temperature and circumference are the most important features for predicting RPW infestation. However, we strongly call for collecting and aggregating more RPW datasets to run more experiments to validate these results and provide more conclusive findings.

scholarly journals Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services

2020 ◽  
Vol 19 ◽  
pp. 117693512091795
Author(s):  
Zeinab Sajjadnia ◽  
Raof Khayami ◽  
Mohammad Reza Moosavi
Keyword(s):  
Breast Cancer ◽  
Cancer Recurrence ◽  
Breast Cancer Recurrence ◽  
Classification Algorithms ◽  
Data Preparation ◽  
Recurrence Prediction ◽  
Null Values ◽  
Error Removal ◽  
The Impact ◽  
F Measure

In recent years, due to an increase in the incidence of different cancers, various data sources are available in this field. Consequently, many researchers have become interested in the discovery of useful knowledge from available data to assist faster decision-making by doctors and reduce the negative consequences of such diseases. Data mining includes a set of useful techniques in the discovery of knowledge from the data: detecting hidden patterns and finding unknown relations. However, these techniques face several challenges with real-world data. Particularly, dealing with inconsistencies, errors, noise, and missing values requires appropriate preprocessing and data preparation procedures. In this article, we investigate the impact of preprocessing to provide high-quality data for classification techniques. A wide range of preprocessing and data preparation methods are studied, and a set of preprocessing steps was leveraged to obtain appropriate classification results. The preprocessing is done on a real-world breast cancer dataset of the Reza Radiation Oncology Center in Mashhad with various features and a great percentage of null values, and the results are reported in this article. To evaluate the impact of the preprocessing steps on the results of classification algorithms, this case study was divided into the following 3 experiments: Breast cancer recurrence prediction without data preprocessing Breast cancer recurrence prediction by error removal Breast cancer recurrence prediction by error removal and filling null values Then, in each experiment, dimensionality reduction techniques are used to select a suitable subset of features for the problem at hand. Breast cancer recurrence prediction models are constructed using the 3 widely used classification algorithms, namely, naïve Bayes, k-nearest neighbor, and sequential minimal optimization. The evaluation of the experiments is done in terms of accuracy, sensitivity, F-measure, precision, and G-mean measures. Our results show that recurrence prediction is significantly improved after data preprocessing, especially in terms of sensitivity, F-measure, precision, and G-mean measures.

scholarly journals COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS: HOLDOUTS AS ACCURACY ESTIMATION

2016 ◽  
Vol 1 (1) ◽  
pp. 13 ◽  
Author(s):  
Debby Erce Sondakh
Keyword(s):  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Decision Rules ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Accuracy Estimation ◽  
F Measure

Penelitian ini bertujuan untuk mengukur dan membandingkan kinerja lima algoritma klasifikasi teks berbasis pembelajaran mesin, yaitu decision rules, decision tree, k-nearest neighbor (k-NN), naïve Bayes, dan Support Vector Machine (SVM), menggunakan dokumen teks multi-class. Perbandingan dilakukan pada efektifiatas algoritma, yaitu kemampuan untuk mengklasifikasi dokumen pada kategori yang tepat, menggunakan metode holdout atau percentage split. Ukuran efektifitas yang digunakan adalah precision, recall, F-measure, dan akurasi. Hasil eksperimen menunjukkan bahwa untuk algoritma naïve Bayes, semakin besar persentase dokumen pelatihan semakin tinggi akurasi model yang dihasilkan. Akurasi tertinggi naïve Bayes pada persentase 90/10, SVM pada 80/20, dan decision tree pada 70/30. Hasil eksperimen juga menunjukkan, algoritma naïve Bayes memiliki nilai efektifitas tertinggi di antara lima algoritma yang diuji, dan waktu membangun model klasiifikasi yang tercepat, yaitu 0.02 detik. Algoritma decision tree dapat mengklasifikasi dokumen teks dengan nilai akurasi yang lebih tinggi dibanding SVM, namun waktu membangun modelnya lebih lambat. Dalam hal waktu membangun model, k-NN adalah yang tercepat namun nilai akurasinya kurang.

A Model of Classification for E-Nose Based on Genetic Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 952-955 ◽  
Author(s):  
Min Jun Jiang ◽  
Yun Xiang Liu ◽  
Jing Xin Yang ◽  
Wan Jun Yu
Keyword(s):  
Genetic Algorithm ◽  
Electronic Nose ◽  
Sensor Data ◽  
Binary Codes ◽  
Classification Algorithms ◽  
Classification Rules ◽  
Fitness Evaluation ◽  
Mutation Operators ◽  
Paper Classification ◽  
F Measure

Electronic nose is an intelligent sensory analyzing instrument which simulates the biological olfaction system. Classification is very important for an electronic nose which is usually seen as the software of E-nose. In this paper, we present a model of classification based on genetic algorithm. Compared with common classification algorithms, genetic algorithm had more powerful flexibility and global searching capability. In this paper classification rules were represented in the form of chromosome by binary codes which are in accordance with the features of sensor data. F-measure was used as fitness evaluation. We also designed efficient crossover, mutation operators.

Infant Pain Detection with Homomorphic Filter and Fuzzy k-NN Classifier

2014 ◽  
Vol 643 ◽  
pp. 183-189
Author(s):  
Muhammad Naufal Mansor ◽  
Ahmad Kadri Junoh ◽  
Amran Ahmed ◽  
Hussin Kamarudin ◽  
Azrini Idris
Keyword(s):  
Local Binary Pattern ◽  
Pathological Condition ◽  
Classification Algorithms ◽  
Performance Measurements ◽  
Time Consumption ◽  
Infant Pain ◽  
Homomorphic Filter ◽  
Pain Detection ◽  
Sensitivity Specificity ◽  
F Measure

Newborn pain is a non-stationary made by babies in reaction to certain circumstances. This infant facial expression can be used to recognize physical or psychology condition of newborn. The goal of this study is to evaluate the performance of illumination levels for infant pain classification. Local Binary Pattern (LBP) features are computed at Fuzzy k-NN classifier. Eight different performance measurements such as Sensitivity, Specificity, Accuracy, Area under Curve (AUC), Cohen's kappa (k), Precession, F-Measure and Time Consumption are performed. Fuzzy k-NN classifier is employed to classify the newborn pain. The outcomes accentuated that the suggested features and classification algorithms can be employed to assist the medical professionals for diagnosing pathological condition of newborn pain.

scholarly journals A Heuristic Checklist for Second Screen Applications

i-com ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Valentin Lohmüller ◽  
Daniel Schmaderer ◽  
Christian Wolff
Keyword(s):  
Focus Group ◽  
User Study ◽  
Harmonic Mean ◽  
Heuristic Evaluation ◽  
Domain Specific ◽  
Second Screen ◽  
Equal Weighting ◽  
F Measure

Abstract This paper presents domain-specific heuristics for second screen applications and the development of a heuristics checklist to enable a more intuitive and structured application of the created heuristics. The heuristics presented were developed on the basis of Nielsen [12] Ten Usability Heuristics in a research-based approach using specific literature and a focus group. In order to evaluate the quality of the derived checklist, a heuristic evaluation of a second screen application with five users was carried out and its results compared to a user study with 20 participants. This resulted in an average validity of 0.5 and a high completeness of 0.74. The harmonic mean of these values results in an F-measure of 0.6 with an equal weighting. This value speaks for a sufficient validity of the created heuristic checklist in the first iteration.

scholarly journals A Hybrid MultiLayer Perceptron Under-Sampling with Bagging Dealing with a Real-Life Imbalanced Rice Dataset

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 291
Author(s):  
Moussa Diallo ◽  
Shengwu Xiong ◽  
Eshete Derb Emiru ◽  
Awet Fesseha ◽  
Aminu Onimisi Abdulsalami ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Real Life ◽  
Class Imbalance ◽  
Classification Algorithms ◽  
Training Set ◽  
Under Sampling ◽  
Mlp Classifier ◽  
F Measure ◽  
Imbalance Dataset ◽  
Bagging Ensemble

Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-life applications are generally imbalanced. Several methods have been proposed to solve the problem of class imbalance. In this paper, we propose a hybrid method combining the preprocessing techniques and those of ensemble learning. The original training set is undersampled by evaluating the samples by stochastic measurement (SM) and then training these samples selected by Multilayer Perceptron to return a balanced training set. The MLPUS (Multilayer perceptron undersampling) balanced training set is aggregated using the bagging ensemble method. We applied our method to the real-life Niger_Rice dataset and forty-four other imbalanced datasets from the KEEL repository in this study. We also compared our method with six other existing methods in the literature, such as the MLP classifier on the original imbalance dataset, MLPUS, UnderBagging (combining random under-sampling and bagging), RUSBoost, SMOTEBagging (Synthetic Minority Oversampling Technique and bagging), SMOTEBoost. The results show that our method is competitive compared to other methods. The Niger_Rice real-life dataset results are 75.6, 0.73, 0.76, and 0.86, respectively, for accuracy, F-measure, G-mean, and ROC with our proposed method. In contrast, the MLP classifier on the original imbalance Niger_Rice dataset gives results 72.44, 0.82, 0.59, and 0.76 respectively for accuracy, F-measure, G-mean, and ROC.

scholarly journals A Comparative Analysis of Classification Algorithms on Diverse Datasets

2018 ◽  
Vol 8 (2) ◽  
pp. 2790-2795 ◽  
Author(s):  
M. Alghobiri
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Comparative Analysis ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
Classification Algorithms ◽  
Kappa Statistics ◽  
Data Sets ◽  
Evaluation Measures ◽  
F Measure

Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

scholarly journals Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rashid Naseem ◽  
Bilal Khan ◽  
Muhammad Arif Shah ◽  
Karzan Wakil ◽  
Atif Khan ◽  
...  
Keyword(s):  
Liver Disease ◽  
Early Stage ◽  
Optimal Solution ◽  
Classification Algorithms ◽  
Early Prediction ◽  
Learning Approaches ◽  
Aged People ◽  
The World ◽  
F Measure ◽  
Better Than

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed.

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