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Journal articles on the topic 'K-fold validation'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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1

Wong, Tzu-Tsung, and Po-Yang Yeh. "Reliable Accuracy Estimates from k-Fold Cross Validation." IEEE Transactions on Knowledge and Data Engineering 32, no. 8 (August 1, 2020): 1586–94. http://dx.doi.org/10.1109/tkde.2019.2912815.

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2

Soper, Daniel S. "Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation." Electronics 10, no. 16 (August 16, 2021): 1973. http://dx.doi.org/10.3390/electronics10161973.

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Selecting a final machine learning (ML) model typically occurs after a process of hyperparameter optimization in which many candidate models with varying structural properties and algorithmic settings are evaluated and compared. Evaluating each candidate model commonly relies on k-fold cross validation, wherein the data are randomly subdivided into k folds, with each fold being iteratively used as a validation set for a model that has been trained using the remaining folds. While many research studies have sought to accelerate ML model selection by applying metaheuristic and other search methods to the hyperparameter space, no consideration has been given to the k-fold cross validation process itself as a means of rapidly identifying the best-performing model. The current study rectifies this oversight by introducing a greedy k-fold cross validation method and demonstrating that greedy k-fold cross validation can vastly reduce the average time required to identify the best-performing model when given a fixed computational budget and a set of candidate models. This improved search time is shown to hold across a variety of ML algorithms and real-world datasets. For scenarios without a computational budget, this paper also introduces an early stopping algorithm based on the greedy cross validation method. The greedy early stopping method is shown to outperform a competing, state-of-the-art early stopping method both in terms of search time and the quality of the ML models selected by the algorithm. Since hyperparameter optimization is among the most time-consuming, computationally intensive, and monetarily expensive tasks in the broader process of developing ML-based solutions, the ability to rapidly identify optimal machine learning models using greedy cross validation has obvious and substantial benefits to organizations and researchers alike.
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3

Wang, Ju E., and Jian Zhong Qiao. "Parameter Selection of SVR Based on Improved K-Fold Cross Validation." Applied Mechanics and Materials 462-463 (November 2013): 182–86. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.182.

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This article firstly uses svm to forecast cashmere price time series. The forecasting result mainly depends on parameter selection. The normal parameter selection is based on k-fold cross validation. The k-fold cross validation is suitable for classification. In this essay, k-fold cross validation is improved to ensure that only the older data can be used to forecast latter data to improve prediction accuracy. This essay trains the cashmere price time series data to build mathematical model based on SVM. The selection of the model parameters are based on improved cross validation. The price of Cashmere can be forecasted by the model. The simulation results show that support vector machine has higher fitting precision in the situation of small samples. It is feasible to forecast cashmere price based on SVM.
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ALPTEKIN, AHMET, and OLCAY KURSUN. "MISS ONE OUT: A CROSS-VALIDATION METHOD UTILIZING INDUCED TEACHER NOISE." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 07 (November 2013): 1351003. http://dx.doi.org/10.1142/s0218001413510038.

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Leave-one-out (LOO) and its generalization, K-Fold, are among most well-known cross-validation methods, which divide the sample into many folds, each one of which is, in turn, left out for testing, while the other parts are used for training. In this study, as an extension of this idea, we propose a new cross-validation approach that we called miss-one-out (MOO) that mislabels the example(s) in each fold and keeps this fold in the training set as well, rather than leaving it out as LOO does. Then, MOO tests whether the trained classifier can correct the erroneous label of the training sample. In principle, having only one fold deliberately labeled incorrectly should have only a small effect on the classifier that uses this bad-fold along with K - 1 good folds and can be utilized as a generalization measure of the classifier. Experimental results on a number of benchmark datasets and three real bioinformatics dataset show that MOO can better estimate the test set accuracy of the classifier.
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Wong, Tzu-Tsung, and Nai-Yu Yang. "Dependency Analysis of Accuracy Estimates in k-Fold Cross Validation." IEEE Transactions on Knowledge and Data Engineering 29, no. 11 (November 1, 2017): 2417–27. http://dx.doi.org/10.1109/tkde.2017.2740926.

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6

Fushiki, Tadayoshi. "Estimation of prediction error by using K-fold cross-validation." Statistics and Computing 21, no. 2 (October 10, 2009): 137–46. http://dx.doi.org/10.1007/s11222-009-9153-8.

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7

Wiens, Trevor S., Brenda C. Dale, Mark S. Boyce, and G. Peter Kershaw. "Three way k-fold cross-validation of resource selection functions." Ecological Modelling 212, no. 3-4 (April 2008): 244–55. http://dx.doi.org/10.1016/j.ecolmodel.2007.10.005.

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8

Nasution, Muhammad Rangga Aziz, and Mardhiya Hayaty. "Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter." Jurnal Informatika 6, no. 2 (September 5, 2019): 226–35. http://dx.doi.org/10.31311/ji.v6i2.5129.

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Salah satu cabang ilmu komputer yaitu pembelajaran mesin (machine learning) menjadi tren dalam beberapa waktu terakhir. Pembelajaran mesin bekerja dengan memanfaatkan data dan algoritma untuk membuat model dengan pola dari kumpulan data tersebut. Selain itu, pembelajaran mesin juga mempelajari bagaimama model yang telah dibuat dapat memprediksi keluaran (output) berdasarkan pola yang ada. Terdapat dua jenis metode pembelajaran mesin yang dapat digunakan untuk analisis sentimen: supervised learning dan unsupervised learning. Penelitian ini akan membandingkan dua algoritma klasifikasi yang termasuk dari supervised learning: algoritma K-Nearest Neighbor dan Support Vector Machine, dengan cara membuat model dari masing-masing algoritma dengan objek teks sentimen. Perbandingan dilakukan untuk mengetahui algoritma mana lebih baik dalam segi akurasi dan waktu proses. Hasil pada perhitungan akurasi menunjukkan bahwa metode Support Vector Machine lebih unggul dengan nilai 89,70% tanpa K-Fold Cross Validation dan 88,76% dengan K-Fold Cross Validation. Sedangkan pada perhitungan waktu proses metode K-Nearest Neighbor lebih unggul dengan waktu proses 0.0160s tanpa K-Fold Cross Validation dan 0.1505s dengan K-Fold Cross Validation.
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9

Y.H. Ahmed, Falah, Yasir Hassan Ali, and Siti Mariyam Shamsuddin. "Using K-Fold Cross Validation Proposed Models for Spikeprop Learning Enhancements." International Journal of Engineering & Technology 7, no. 4.11 (October 2, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i4.11.20790.

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Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte’s algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets.
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10

Jiang, Gaoxia, and Wenjian Wang. "Error estimation based on variance analysis of k -fold cross-validation." Pattern Recognition 69 (September 2017): 94–106. http://dx.doi.org/10.1016/j.patcog.2017.03.025.

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11

Rodriguez, J. D., A. Perez, and J. A. Lozano. "Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 3 (March 2010): 569–75. http://dx.doi.org/10.1109/tpami.2009.187.

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12

Peryanto, Ari, Anton Yudhana, and Rusydi Umar. "Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation." Journal of Applied Informatics and Computing 4, no. 1 (May 13, 2020): 45–51. http://dx.doi.org/10.30871/jaic.v4i1.2017.

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Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.
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13

Boxell, Levi. "K-fold Cross-Validation and the Gravity Model of Bilateral Trade." Atlantic Economic Journal 43, no. 2 (May 7, 2015): 289–300. http://dx.doi.org/10.1007/s11293-015-9459-1.

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14

Hadjisolomou, Ekaterini, Konstantinos Stefanidis, Herodotos Herodotou, Michalis Michaelides, George Papatheodorou, and Eva Papastergiadou. "Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks." Water 13, no. 11 (June 4, 2021): 1590. http://dx.doi.org/10.3390/w13111590.

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Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.
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15

Grimm, Kevin J., Gina L. Mazza, and Pega Davoudzadeh. "Model Selection in Finite Mixture Models: A k-Fold Cross-Validation Approach." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 2 (December 5, 2016): 246–56. http://dx.doi.org/10.1080/10705511.2016.1250638.

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16

Widyaningsih, Yekti, Graceilla Puspita Arum, and Kevin Prawira. "APLIKASI K-FOLD CROSS VALIDATION DALAM PENENTUAN MODEL REGRESI BINOMIAL NEGATIF TERBAIK." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 2 (June 1, 2021): 315–22. http://dx.doi.org/10.30598/barekengvol15iss2pp315-322.

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Publikasi ilmiah merupakan salah satu indikator penilaian terhadap kualitas akademisi. Tetapi tidak dapat dipungkiri pembuatan publikasi ilmiah bukanlah suatu hal yang mudah, karena membutuhkan proses pembuatan dan proses penelaahan yang rumit. Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang memengaruhi banyaknya publikasi ilmiah yang dihasilkan oleh mahasiswa PhD Biokimia tahun 1997. Karena variabel dependen merupakan count data, metode analisis yang digunakan adalah Regresi Poisson. Namun karena data mengalami overdispersi, akan digunakan Regresi Binomial Negatif. Perbandingan beberapa model Regresi Poisson dan Binomial Negatif dilakukan untuk menentukan model terbaik dengan k-fold cross validation sebagai validasi model. Hasil penelitian menunjukkan bahwa model terbaik yang didapatkan adalah model Regresi Binomial Negatif dengan variabel independen jenis kelamin, status pernikahan, banyaknya anak dibawah 5 tahun, prestise, dan banyaknya artikel oleh mentor dalam 3 tahun terakhir.
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Mabuni, D., and S. Aquter Babu. "High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (January 10, 2021): 105–10. http://dx.doi.org/10.35940/ijitee.c8403.0110321.

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In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to obtain reliable and statistically correct classification results there is a need to apply the same algorithm on different pairs of training and validation datasets. To overcome the problem of the usage of only a single training dataset and a single testing dataset the existing k-fold cross validation technique uses cross validation plan for obtaining increased decision tree classification accuracy results. In this paper a new cross validation technique called prime fold is proposed and it is experimentally tested thoroughly and then verified correctly using many bench mark UCI machine learning datasets. It is observed that the prime fold based decision tree classification accuracy results obtained after experimentation are far better than the existing techniques of finding decision tree classification accuracies.
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18

Normawati, Dwi, and Dewi Pramudi Ismi. "K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining." Signal and Image Processing Letters 1, no. 2 (July 19, 2019): 23–35. http://dx.doi.org/10.31763/simple.v1i2.3.

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Coronary heart disease is a disease that often causes human death, occurs when there is atherosclerosis blocking blood flow to the heart muscle in the coronary arteries. The doctor's referral method for diagnosing coronary heart disease is coronary angiography, but it is invasive, high risk and expensive. The purpose of this study is to analyze the effect of implementing the k-Fold Cross Validation (CV) dataset on the rule-based feature selection to diagnose coronary heart disease, using the Cleveland heart disease dataset. The research conducted a feature selection using a medical expert-based (MFS) and computer-based method, namely the Variable Precision Rough Set (VPRS), which is the development of the Rough Set theory. Evaluation of classification performance using the k-Fold method of 10-Fold, 5-Fold and 3-Fold. The results of the study are the number of attributes of the feature selection results are different in each Fold, both for the VPRS and MFS methods, for accuracy values obtained from the average accuracy resulting from 10-Fold, 5-Fold and 3-Fold. The result was the highest accuracy value in the VPRS method 76.34% with k = 5, while the MTF accuracy was 71.281% with k = 3. So, the k-fold implementation for this case is less effective, because the division of data is still structured, according to the order of records that apply in each fold, while the amount of testing data is too small and too structured. This affects the results of the accuracy because the testing rules are not thoroughly represented
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Jiao, Long, Xiaofei Wang, Shan Bing, Zhiwei Xue, and Hua Li. "QSPR study of supercooled liquid vapour pressures of PBDEs by using molecular distance-edge vector index." Journal of the Serbian Chemical Society 80, no. 4 (2015): 499–508. http://dx.doi.org/10.2298/jsc140716087j.

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The quantitative structure property relationship (QSPR) for supercooled liquid vapour pressures (PL) of PBDEs was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and lgPL was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave-one-out cross validation and k-fold cross validation were carried out to assess the prediction ability of the developed models. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and k-fold cross validation is 9.95 and 9.05 respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and k-fold cross validation is 8.75 and 8.31 respectively. It is demonstrated the established models are practicable for predicting the lgPL of PBDEs. The MDEV index is quantitatively related to the lgPL of PBDEs. MLR and L-ANN are practicable for modeling this relationship. Compared with MLR, ANN shows slightly higher prediction accuracy. Subsequently, an MLR model, which regression equation is lgPL = 0.2868 M11 - 0.8449 M12 - 0.0605, and an ANN model, which is a two inputs linear network, were developed. The two models can be used to predict the lgPL of each PBDE.
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Pohjankukka, Jonne, Tapio Pahikkala, Paavo Nevalainen, and Jukka Heikkonen. "Estimating the prediction performance of spatial models via spatial k-fold cross validation." International Journal of Geographical Information Science 31, no. 10 (July 5, 2017): 2001–19. http://dx.doi.org/10.1080/13658816.2017.1346255.

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21

Rizki, Yoze, Reny Medikawati Taufiq, Harun Mukhtar, and Dinia Putri. "Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN." IT Journal Research and Development 5, no. 2 (January 6, 2021): 215–25. http://dx.doi.org/10.25299/itjrd.2021.vol5(2).5831.

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Motif tenun melayu sangat beragam. Keberagaman ini membuat sulit membedakan motif-motif kain tenun tersebut. Klasifikasi data diperlukan untuk mengidentifikasi karakteristik objek yang terkandung dalam basis data agar kemudian dikategorikan ke dalam kelompok yang berbeda. Tujuan penelitian yang dicapai dalam penelitian ini yaitu untuk mengetahui performa pengenalan dan klasifikasi motif tenun melayu menggunakan Faster R-CNN dengan model arsitektur VGG, dengan cara mengukur persentase dari tingkat akurasi, presisi, dan recall yang akan divalidasi menggunakan K-Fold Cross Validation. Penelitian ini menggunakan algoritma deteksi objek Faster R-CNN sebagai metode pengenalan dan klasifikasi pola kain berbasis citra digital. Faster R-CNN merupakan salah satu metode yang digunakan untuk mengenali objek pada citra digital. Kemampuan pengenalan objek ini digenean untuk mengenali dan mengklasifikasi motif-motif kain tenun melayu. Jumlah dataset yang digunakan berjumlah 100 citra yang diacak untuk masing-masing dari 5 (lima) fold pada K-fold cross validation. Data tersebut dibagi menjadi 80 data train dan 20 data test. Setelah dilakukan persiapan data, pre-processing, serta implementasi, dilakukan pengujian dengan hasil bahwa dari data latih yang berupa citra kain tenun melayu, didapatkan skor rata-rata training loss dari step pertama hingga step terakhir sebesar 1,915. Klasifikasi karakteristik pengenalan motif tenun melayu menggunakan Metode deteksi objek Faster R-CNN melalui validasi K-Fold Cross Validation dengan nilai k=5, didapatkan akurasi 82.14%, presisi 91.38% dan recall 91.36%.
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Nasution, Darnisa Azzahra, Hidayah Husnul Khotimah, and Nurul Chamidah. "Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN." Computer Engineering, Science and System Journal 4, no. 1 (January 31, 2019): 78. http://dx.doi.org/10.24114/cess.v4i1.11458.

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Abstrak— Rentang nilai yang tidak seimbang pada setiap atribut dapat mempengaruhi kualitas hasil data mining. Untuk itu diperlukan adanya praproses data. Praproses ini diharapkan dapat meningkatkatkan keakuratan hasil dari pengklasifikasian dataset wine. Metode praproses yang digunakan adalah transformasi data dengan normalisasi. Ada tiga cara yang dilakukan dalam transformasi data dengan normalisasi, yaitu min-max normalization, z-score normalization, dan decimal scaling. Data yang telah diproses dari setiap metode normalisasi akan dibandingan untuk melihat hasil akurasi terbaik klasifikasi dengan menggunakan algoritama K-NN. K yang digunakan dalam perbandingan adalah 1, 3, 5, 7, 9, 11. Sebelum dilakukan pengklasifikasian dataset wine yang telah dinormalisasi dibagi menjadi data uji dan data latih dengan k-fold cross validation. Pembagian data menggunakan k sama dengan 10. Hasil pengujian klasifikasi dengan algoritma K-NN menunjukkan, bahwa akurasi terbaik terletak pada dataset wine yang telah dinormalisasi menggunakan metode min-max normalization dengan K = 1 sebesar 65,92%. Rata-rata yang diperoleh, yaitu 59,68%. Keywords— Normalisasi, K-fold cross validation, K-NN
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Wang, Yu, and Jihong Li. "Credible Intervals for Precision and Recall Based on a K-Fold Cross-Validated Beta Distribution." Neural Computation 28, no. 8 (August 2016): 1694–722. http://dx.doi.org/10.1162/neco_a_00857.

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In typical machine learning applications such as information retrieval, precision and recall are two commonly used measures for assessing an algorithm's performance. Symmetrical confidence intervals based on K-fold cross-validated t distributions are widely used for the inference of precision and recall measures. As we confirmed through simulated experiments, however, these confidence intervals often exhibit lower degrees of confidence, which may easily lead to liberal inference results. Thus, it is crucial to construct faithful confidence (credible) intervals for precision and recall with a high degree of confidence and a short interval length. In this study, we propose two posterior credible intervals for precision and recall based on K-fold cross-validated beta distributions. The first credible interval for precision (or recall) is constructed based on the beta posterior distribution inferred by all K data sets corresponding to K confusion matrices from a K-fold cross-validation. Second, considering that each data set corresponding to a confusion matrix from a K-fold cross-validation can be used to infer a beta posterior distribution of precision (or recall), the second proposed credible interval for precision (or recall) is constructed based on the average of K beta posterior distributions. Experimental results on simulated and real data sets demonstrate that the first credible interval proposed in this study almost always resulted in degrees of confidence greater than 95%. With an acceptable degree of confidence, both of our two proposed credible intervals have shorter interval lengths than those based on a corrected K-fold cross-validated t distribution. Meanwhile, the average ranks of these two credible intervals are superior to that of the confidence interval based on a K-fold cross-validated t distribution for the degree of confidence and are superior to that of the confidence interval based on a corrected K-fold cross-validated t distribution for the interval length in all 27 cases of simulated and real data experiments. However, the confidence intervals based on the K-fold and corrected K-fold cross-validated t distributions are in the two extremes. Thus, when focusing on the reliability of the inference for precision and recall, the proposed methods are preferable, especially for the first credible interval.
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Asmara, Rosa Andrie, Arief Prasetyo, Siska Stevani, and Ratih Indri Hapsari. "Prediksi Banjir Lahar Dingin pada Lereng Merapi menggunakan Data Curah Hujan dari Satelit." Jurnal Informatika Polinema 7, no. 2 (February 23, 2021): 35–42. http://dx.doi.org/10.33795/jip.v7i2.494.

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Banjir lahar dingin merupakan sekumpulan lahar yang dimuntahkan oleh gunung berapi dan sampai ke permukaan yang lebih rendah dengan bantuan atau dorongan dari air hujan. Akibatnya, air hujan yang membawa serta material-material vulkanik dari lahar ini akan menerjang lahan yang berada di bawahnya ataupun pemukiman penduduk dan banyaknya kerusakan atapun dampak-dampak lain yang akan dihasilkan oleh banjir lahar dingin ini. Faktor yang menyebabkan banjir lahar adalah intensitas atau curah hujan (mm/jam) dan akumulasi hujan (mm/7hari). Terjadinya banjir lahar dapat dideteksi oleh beberapa alat salah satunya adalah Geofon. Alat sering rusak dan hanyut karena dipasang atau ditempatkan pada permukaan tanah disetiap stasiun sungai, dan pada saat terjadinya banjir lahar hingga sampai ke permukaan yang lebih rendah maka alat tersebut tidak dapat mengirimkan informasi getaran. Oleh karena itu pada penelitian ini di buat sebuah sistem untuk membantu sensor Geofon dalam memprediksi banjir lahar pada kawasan Lereng Merapi. Sistem akan mengeluarkan status getaran yang terdiri dari 4 kelas yaitu banjir rendah, banjir sedang, banjir tinggi dan tidak terjadi banjir lahar dengan memperhitungkan atribut curah hujan dan akumulasi hujan dari satelit menggunakan metode K-NN (K-Nearest Neighbor). Pemilihan nilai K pada algoritma K-NN menjadi hal yang penting karena akan mempengaruhi kinerja dari algoritma K-NN pada sistem prediksi banjir lahar, oleh karena itu perlu diketahui berapa nilai K dan tingkat akurasinya. Metode 10-Fold Cross Validation dan Uji Akurasi digunakan untuk mengetahui nilai K Optimal pada tiap lokasi penelitian yaitu Gendol, Putih 1 dan Putih 2. Berdasarkan hasil pengujian yang didapat adalah pada lokasi Gendol dan Putih 1 menggunakan 3-NN dengan akurasi rata-rata 72.307% dan 81.429%, lokasi Putih 2 menggunakan 1-NN dengan akurasi rata-rata 86.955%. Data pengujian pada lokasi Gendol menggunakan data 1-Fold Cross Validation dengan akurasi 3-NN 92.31%, Putih 1 data 8-Fold Cross Validation dengan akurasi 3-NN 95.24%, dan Putih 2 data 10-Fold Cross Validation dengan akurasi 1-NN 91.3%.
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Triba, Mohamed N., Laurence Le Moyec, Roland Amathieu, Corentine Goossens, Nadia Bouchemal, Pierre Nahon, Douglas N. Rutledge, and Philippe Savarin. "PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters." Molecular BioSystems 11, no. 1 (2015): 13–19. http://dx.doi.org/10.1039/c4mb00414k.

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In some cases, quality parameter values (the number of significant components,Q2, CV-ANOVAp-value,…) of PLS/OPLS models calculated with K-fold cross-validation can be strongly determined by the composition of the different validation subsets.
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SUPARTINI, IDA AYU MADE, I. KOMANG GDE SUKARSA, and I. GUSTI AYU MADE SRINADI. "ANALISIS DISKRIMINAN PADA KLASIFIKASI DESA DI KABUPATEN TABANAN MENGGUNAKAN METODE K-FOLD CROSS VALIDATION." E-Jurnal Matematika 6, no. 2 (May 31, 2017): 106. http://dx.doi.org/10.24843/mtk.2017.v06.i02.p154.

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Tabanan Regency is one of the eight regencies and one municipality in Bali Province. Administratively, it is divided into 10 districs and villages. There are rural areas and urban areas in the regions. Discriminant analysis is a technique related to the separation of objects into different groups that have been set previously. The purpose of this research is to classify villlages in Tabanan Regency into urban or rural groups with discriminant analysis. Linear discriminant analysis assumes that the covariance matrix of the two groups are equals, if the assumption of equality of covariance matrix is violated, quadratic discriminant analysis can be used for classification. This research uses k-fold crosss validation method for calculating the accuracy of quadratic discriminant function where . Quadratic discriminant function is obtained by with the smallest APER value (). All of classification results are stable and consistence.
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Baykan, Nurdan, and Nihat Yılmaz. "A Mineral Classification System with Multiple Artificial Neural Network Using K-Fold Cross Validation." Mathematical and Computational Applications 16, no. 1 (April 1, 2011): 22–30. http://dx.doi.org/10.3390/mca16010022.

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Wong, Tzu-Tsung. "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation." Pattern Recognition 48, no. 9 (September 2015): 2839–46. http://dx.doi.org/10.1016/j.patcog.2015.03.009.

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Moreno-Torres, J. G., J. A. Saez, and F. Herrera. "Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation." IEEE Transactions on Neural Networks and Learning Systems 23, no. 8 (August 2012): 1304–12. http://dx.doi.org/10.1109/tnnls.2012.2199516.

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Fang, Liang, Shiqin Liu, and Zhiyong Huang. "Uncertain Johnson–Schumacher growth model with imprecise observations and k-fold cross-validation test." Soft Computing 24, no. 4 (May 28, 2019): 2715–20. http://dx.doi.org/10.1007/s00500-019-04090-4.

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Asrol, Muhammad, Petir Papilo, and Fergyanto E. Gunawan. "Support Vector Machine with K-fold Validation to Improve the Industry’s Sustainability Performance Classification." Procedia Computer Science 179 (2021): 854–62. http://dx.doi.org/10.1016/j.procs.2021.01.074.

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Irfani, Faizal Fakhri. "ANALISIS SENTIMEN REVIEW APLIKASI RUANGGURU MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE." JBMI (Jurnal Bisnis, Manajemen, dan Informatika) 16, no. 3 (February 28, 2020): 258–66. http://dx.doi.org/10.26487/jbmi.v16i3.8607.

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Analisis sentiment review aplikasi Ruangguru merupakan proses menganalisa, memahami dan mengklasifikasikan suatu penilaian yang dikeluarkan masyarakat terhadap entitas aplikasi Ruangguru. Data penelitian ini diambil dari website google play store, data yang diambil yaitu data teks review dengan jumlah 2000 review. Data diklasifikasikan dengan menggunakan algoritma Support Vector Machine, dan dilakukan pengujian menggunakan kombinasi dari pembagian data latih dan data uji, serta menggunakan sistem K-Fold Cross-Validation. pengujian menggunakan kernel linear menghasilkan akurasi tertinggi dengan nilai 0.897. kombinasi data training 60% dan data testing 40% menghasilkan akurasi tertinggi sebesar 0.900. Pengujian dengan menggunakan sistem k-fold, akurasi tertinggi berada di nilai k-fold 6, 9, dan 10 dengan nilai akurasi sebesar 0.902. Namun pada k-fold 10, tingkat presisi nya lebih tinggi dibanding nilai k-fold lainnya dengan nilai presisi sebesar 0.903. Tingkat akurasi dalam penelitian ini tinggi berada di kisaran 90%. Hasil dari beberapa pengujian menunjukan bahwa sentimen masyarakat terhadap aplikasi Ruangguru cenderung positif.
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Sandag, Green Arther. "Exploratory Data Analysis Towards Terrorist Activity In Indonesia Using Machine Learning Techniques." Abstract Proceedings International Scholars Conference 7, no. 1 (December 18, 2019): 1749–60. http://dx.doi.org/10.35974/isc.v7i1.1628.

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Introduction: Terrorism Activity is the subject of the talks in various countries, especially in Indonesia. The activities of terrorism are carried out in various ways using suicide bombs, violent action that aimed to demoralize by creating fear to the society and national security. In Indonesia, according to Kompas news website recorded there were 10 suicide bombings occurred in the past 6 years and took many casualties in every event. With this, it certainly gives a threat to the people in Indonesia in terms of physical, moral and even in terms of national security Methods: To overcome this problem, it is necessary to increase the national security so that terrorism can be prevented and it will not happen again. This study is aimed to conduct an exploratory data analysis and predict terrorist activity in Indonesia using K-Nearest Neighbor (KNN), and k-fold cross-validation. In this research, data selection, data cleaning, data reduction were carried out and feature selectionprocess which aimed to find out the most influential data attributes. Results:According to the analysis, the researcher proved the result using the K-NN algorithm independentlyis different from the result of K-NN algorithm testing which added the use of k-fold cross-validationin predicting terrorist activity in Indonesia. The evidenced of the result obtained by doing a comparison between the best value of k, found that value of k = 8 values is the best in this study by generating the value of accuracyusing k-fold cross-validationof 88.86%, recall73.69%, precision 74.44% and RMSE 0.333. While independent testing with k = 8 produces an accuracy value of 88.82%, recall 64.29%, precision 72.42% and RMSE value (root mean square error)of 0.308. Discussion:The results obtained in this study expected to be a reference for other researchers who will conduct further research related to terrorist activities in Indonesia either performing analytical activities or making an application to predict terrorist activities and additional information from the research that had performed will provide advice for security forces to enhance national security.
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She, Lu, Hankui K. Zhang, Zhengqiang Li, Gerrit de Leeuw, and Bo Huang. "Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations." Remote Sensing 12, no. 24 (December 17, 2020): 4125. http://dx.doi.org/10.3390/rs12244125.

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Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed.
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Jayawardanu, Ivana Herliana W., and Seng Hansun. "Rancang Bangun Sistem Pakar untuk Deteksi Dini Katarak Menggunakan Algoritma C4.5." Jurnal ULTIMA Computing 7, no. 2 (August 1, 2016): 48–58. http://dx.doi.org/10.31937/sk.v7i2.232.

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In 2010, 51% of 39 million blindness are caused by cataract. In 2013, there are 1.8% of 1.027.763 Indonesian people who suffered from cataract. Half of them are not treated yet due to their ignorance on the cataract disease. Therefore, in this research, we tried to build a system that can detect early cataract disease as the ophthalmologist would do. The system will use C4.5 algorithm that receives 150 training data set as an input, resulting in a set of rules which can be used as decision factors. To test the system, k-fold cross validation technique is been used with k equals to 10. From the analysis result, the accuracy of the system is 93.2% to detect cataract disease and 80.5% to detect the type of cataract disease one might suffered. Index terms-C4.5 algorithm, cataract, k-fold cross validation, machine learning
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Rhomadhona, Herfia, and Jaka Permadi. "Klasifikasi Berita Kriminal Menggunakan Naïve Bayes Classifier (NBC) dengan Pengujian K-Fold Cross Validation." Jurnal Sains dan Informatika 5, no. 2 (December 2, 2019): 108–17. http://dx.doi.org/10.34128/jsi.v5i2.177.

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Berita kriminalitas merupakan berita yang selalu menjadi trending topik di setiap media massa, khususnya media massa online. Media massa online terlah menyediakan beberapa fasilitas untuk mempermudah masyarakan dalam mencari sebuah berita berdasarkan topik. Media massa online melabeli suatu berita berdasarkan kategorinya. Namun, media massa online tidak memberikan sub kategori pada berita tersebut. Sebagai contoh jika seorang pengguna membuka kategori kriminal, maka yang ditampilkan adalah semua jenis berita kriminal tanpa memberikan informasi yang spesifik dari jenis kriminalitasnya. Permasalahan tersebut dapat diatasi dengan mengklasifikasikan berita kriminalitas berdasarkan subkategori. Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC) untuk mengklasifikasi berita berdasarkan sub kategorinya. Adapun subkategori terbagi kedalam 5 kategori yaitu korupsi, narkoba, pencurian, pemerkosaan dan pembunuhan. Penelitian ini bertujuan untuk mengetahui kemampuan NBC dalam mengklasifikasi berita dengan melakukan pengujian menggunakan teknik K-Fold Cross Validation dengan nilai K dari 3 sampai 10. Hasil pengujian menyatakan bahwa NBC memiliki kemampuan dalam klasifikasi berita kriminal dengan nilai precision sebesar 98,53 %, nilai recall sebesar 98,44 % dan nilai accuracy sebesar 99,38 %.
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Jiang, Ping, and Jiejie Chen. "Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation." Neurocomputing 198 (July 2016): 40–47. http://dx.doi.org/10.1016/j.neucom.2015.08.118.

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Camacho, José, and Alberto Ferrer. "Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects." Journal of Chemometrics 26, no. 7 (May 1, 2012): 361–73. http://dx.doi.org/10.1002/cem.2440.

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Camacho, José, and Alberto Ferrer. "Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects." Chemometrics and Intelligent Laboratory Systems 131 (February 2014): 37–50. http://dx.doi.org/10.1016/j.chemolab.2013.12.003.

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Wei, Jie, and Hui Chen. "Determining the number of factors in approximate factor models by twice K-fold cross validation." Economics Letters 191 (June 2020): 109149. http://dx.doi.org/10.1016/j.econlet.2020.109149.

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Abellán-García, Joaquín. "K-fold Validation Neural Network Approach for Predicting the One-Day Compressive Strength of UHPC." Advances in Civil Engineering Materials 10, no. 1 (May 10, 2021): 20200055. http://dx.doi.org/10.1520/acem20200055.

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Ziggah, Yao Yevenyo, Hu Youjian, Alfonso Rodrigo Tierra, and Prosper Basommi Laari. "Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana." Earth Sciences Research Journal 23, no. 1 (January 1, 2019): 67–77. http://dx.doi.org/10.15446/esrj.v23n1.63860.

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The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different datums has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.
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Kholik, Abdul, Agus Harjoko, and Wahyono Wahyono. "Classification of Traffic Vehicle Density Using Deep Learning." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 1 (January 31, 2020): 69. http://dx.doi.org/10.22146/ijccs.50376.

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The volume density of vehicles is a problem that often occurs in every city, as for the impact of vehicle density is congestion. Classification of vehicle density levels on certain roads is required because there are at least 7 vehicle density level conditions. Monitoring conducted by the police, the Department of Transportation and the organizers of the road currently using video-based surveillance such as CCTV that is still monitored by people manually. Deep Learning is an approach of synthetic neural network-based learning machines that are actively developed and researched lately because it has succeeded in delivering good results in solving various soft-computing problems, This research uses the convolutional neural network architecture. This research tries to change the supporting parameters on the convolutional neural network to further calibrate the maximum accuracy. After the experiment changed the parameters, the classification model was tested using K-fold cross-validation, confusion matrix and model exam with data testing. On the K-fold cross-validation test with an average yield of 92.83% with a value of K (fold) = 5, model testing is done by entering data testing amounting to 100 data, the model can predict or classify correctly i.e. 81 data.
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Zhu, Ruijin, Weilin Guo, and Xuejiao Gong. "Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model." Energies 12, no. 7 (March 29, 2019): 1220. http://dx.doi.org/10.3390/en12071220.

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Short-term photovoltaic power forecasting is of great significance for improving the operation of power systems and increasing the penetration of photovoltaic power. To improve the accuracy of short-term photovoltaic power forecasting, an ensemble-model-based short-term photovoltaic power prediction method is proposed. Firstly, the quartile method is used to process raw data, and the Pearson coefficient method is utilized to assess multiple features affecting the short-term photovoltaic power output. Secondly, the structure of the ensemble model is constructed, and a k-fold cross-validation method is used to train the submodels. The prediction results of each submodel are merged. Finally, the validity of the proposed approach is verified using an actual data set from State Power Investment Corporation Limited. The simulation results show that the quartile method can find outliers which contributes to processing the raw data and improving the accuracy of the model. The k-fold cross-validation method can effectively improve the generalization ability of the model, and the ensemble model can achieve higher prediction accuracy than a single model.
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Nam, Jung Soo, Cho Rok Na, Hyoung Han Jo, Jun Yeob Song, Tae Ho Ha, and Sang Won Lee. "Injection-moulded lens form error prediction using cavity pressure and temperature signals based on k-fold cross validation." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232, no. 5 (June 16, 2016): 928–34. http://dx.doi.org/10.1177/0954405416654421.

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This article discusses the development of lens form error prediction models using in-process cavity pressure and temperature signals based on a k-fold cross-validation method. In a series of lens injection moulding experiments, the built-in-sensor mould is used, the in-process cavity pressure and temperature signals are captured and the lens form errors are measured. Then, three features including maximum pressure, holding pressure and maximum temperature are identified from the measured cavity pressure and temperature profiles, and the lens form error prediction models are formulated based on a response surface methodology. In particular, the k-fold cross-validation approach is adopted in order to improve the prediction accuracy. It is demonstrated that the lens form error prediction models can be practically used for diagnosing the quality of injection-moulded lenses in an industrial site.
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Batista, Ana Catharina, Virgínia Santos, João Afonso, Cristina Guedes, Jorge Azevedo, Alfredo Teixeira, and Severiano Silva. "Evaluation of an Image Analysis Approach to Predicting Primal Cuts and Lean in Light Lamb Carcasses." Animals 11, no. 5 (May 12, 2021): 1368. http://dx.doi.org/10.3390/ani11051368.

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Carcass dissection is a more accurate method for determining the composition of a carcass; however, it is expensive and time-consuming. Techniques like VIA are of great interest once they are objective and able to determine carcass contents accurately. This study aims to evaluate the accuracy of a flexible VIA system to determine the weight and yield of the commercial value of carcass cuts of light lamb. Photos from 55 lamb carcasses are taken and a total of 21 VIA measurements are assessed. The half-carcasses are divided into six primal cuts, grouped according to their commercial value: high-value (HVC), medium-value (MVC), low-value (LVC) and all of the cuts (AllC). K-folds cross-validation stepwise regression analyses are used to estimate the weights of the cuts in the groups and their lean meat yields. The models used to estimate the weight of AllC, HVC, MVC and LVC show similar results and a k-fold coefficient of determination (k-fold-R2) of 0.99 is achieved for the HVC and AllC predictions. The precision of the weight and yield of the three prediction models varies from low to moderate, with k-fold-R2 results between 0.186 and 0.530, p < 0.001. The prediction models used to estimate the total lean meat weight are similar and low, with k-fold-R2 results between 0.080 and 0.461, p < 0.001. The results confirm the ability of the VIA system to estimate the weights of parts and their yields. However, more research is needed on estimating lean meat yield.
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Valavi, Roozbeh, Jane Elith, José J. Lahoz‐Monfort, and Gurutzeta Guillera‐Arroita. "block CV : An r package for generating spatially or environmentally separated folds for k ‐fold cross‐validation of species distribution models." Methods in Ecology and Evolution 10, no. 2 (November 8, 2018): 225–32. http://dx.doi.org/10.1111/2041-210x.13107.

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Sumarlin, Sumarlin, and Dewi Anggraini. "IMPLEMENTASI K-NEAREST NEIGHBORD PADA RAPIDMINER UNTUK PREDIKSI KELULUSAN MAHASISWA." High Education of Organization Archive Quality: Jurnal Teknologi Informasi 10, no. 1 (May 31, 2018): 35–41. http://dx.doi.org/10.52972/hoaq.vol10no1.p35-41.

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Data on graduate students is an important part in determining the quality of a private and public university. Graduate data is included in important assessments in the accreditation process. Data from Uyelindo Kupang STIKOM graduates every year will continue to grow and accumulate like neglected data because it is rarely used. To maximize student data into information that can be used by universities, the data must be processed in this case used as training data in a study using data mining to obtain information in the form of predictions of graduation from Kupang Uyelindo STIKOM students. The method used in this study is K-Nearest Neighbor using rapidminer software to measure K-Nearest Neighbor's accuracy against student graduate data. The criteria used were in the form of student names, gender, cumulative achievement index (GPA) from semester 1 to 6. In applying the K-Nearest Neighbor algorithm can be used to produce predictions of student graduation. To measure the performance of the k-nearest neighbor algorithm, the Cross Validation, Confusion Matrix and ROC Curves methods are used, in this study using a 5-fold cross validation to predict student graduation. From 100 student dataset records Uyelindo Kupang STIKOM graduates obtained accuracy rate reached 82% and included a very good classification because it has an AUC value between 0.90-1.00, which is 0.971, so it can be concluded that the accuracy of testing of student graduation models using K-Nearest Neighbor (K-NN) algorithm is influenced by the number of data clusters. Accuracy and the highest AUC value of 5-fold validation is to cluster data k = 4 with the accuracy value of 90%.
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Nurhopipah, Ade, and Uswatun Hasanah. "Dataset Splitting Techniques Comparison For Face Classification on CCTV Images." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 4 (October 31, 2020): 341. http://dx.doi.org/10.22146/ijccs.58092.

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The performance of classification models in machine learning algorithms is influenced by many factors, one of which is dataset splitting method. To avoid overfitting, it is important to apply a suitable dataset splitting strategy. This study presents comparison of four dataset splitting techniques, namely Random Sub-sampling Validation (RSV), k-Fold Cross Validation (k-FCV), Bootstrap Validation (BV) and Moralis Lima Martin Validation (MLMV). This comparison is done in face classification on CCTV images using Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM) algorithm. This study is also applied in two image datasets. The results of the comparison are reviewed by using model accuracy in training set, validation set and test set, also bias and variance of the model. The experiment shows that k-FCV technique has more stable performance and provide high accuracy on training set as well as good generalizations on validation set and test set. Meanwhile, data splitting using MLMV technique has lower performance than the other three techniques since it yields lower accuracy. This technique also shows higher bias and variance values and it builds overfitting models, especially when it is applied on validation set.
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Lee Kar Ming, Jesse, Farah Saleena Taip, Mohd Shamsul Anuar, Samsul Bahari Mohd Noor, and Zalizawati Abdullah. "Artificial Neural Network Topology Optimization using K-Fold Cross Validation for Spray Drying of Coconut Milk." IOP Conference Series: Materials Science and Engineering 778 (May 1, 2020): 012094. http://dx.doi.org/10.1088/1757-899x/778/1/012094.

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