Combining kNN Imputation and Bootstrap Calibrated

2010 ◽  
Vol 6 (4) ◽  
pp. 61-73 ◽  
Author(s):  
Yongsong Qin ◽  
Shichao Zhang ◽  
Chengqi Zhang
Keyword(s):  
Confidence Intervals ◽  
Incomplete Data ◽  
Asymptotic Theory ◽  
Nearest Neighbor ◽  
Normal Approximation ◽  
Simple Procedure ◽  
Important Research ◽  
K Nearest Neighbor ◽  
Research Topics ◽  
Data Discovery

The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the kNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with kNN imputation method.

Combining kNN Imputation and Bootstrap Calibrated

2011 ◽  
pp. 278-289
Author(s):  
Yongsong Qin ◽  
Shichao Zhang ◽  
Chengqi Zhang
Keyword(s):  
Confidence Intervals ◽  
Incomplete Data ◽  
Nearest Neighbor ◽  
Simple Procedure ◽  
Imputation Method ◽  
Important Research ◽  
K Nearest Neighbor ◽  
Data Discovery ◽  
Industrial Data ◽  
Simulation Results

The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This chapter studies a new estimation for missing (or incomplete) data that is a combination of the kNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN imputation, but also inherits the advantages of the EL method compared to the normal approximation method. Simulation results demonstrate that the bootstrap calibrated EL method performs quite well in estimating confidence intervals for the imputed data with kNN imputation method.

Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines

1997 ◽  
Vol 08 (03) ◽  
pp. 301-315 ◽  
Author(s):  
Marcel J. Nijman ◽  
Hilbert J. Kappen
Keyword(s):  
Symmetry Breaking ◽  
Incomplete Data ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Boltzmann Machine ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Input Space ◽  
Learning Rules ◽  
Radial Basis

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.

scholarly journals K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng Lu ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Clustering Analysis ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Interval Data ◽  
Similarity Function ◽  
K Nearest Neighbor ◽  
Partial Data ◽  
Missing Attributes ◽  
Ap Clustering

The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based onK-nearest neighbor intervals (KNNI) for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

Predicting Protein Subcellular Localization Using the Algorithm of Increment of Diversity Combined with Weighted K-Nearest Neighbor

2013 ◽  
Vol 765-767 ◽  
pp. 3099-3103 ◽  
Author(s):  
Ze Yue Wu ◽  
Yue Hui Chen
Keyword(s):  
Subcellular Localization ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Research Field ◽  
Important Research ◽  
Protein Subcellular Localization ◽  
K Nearest Neighbor ◽  
Prediction Ability ◽  
New Approach ◽  
Increment Of Diversity

Protein subcellular localization is an important research field of bioinformatics. In this paper, we use the algorithm of the increment of diversity combined with weighted K nearest neighbor to predict protein in SNL6 which has six subcelluar localizations and SNL9 which has nine subcelluar localizations. We use the increment of diversity to extract diversity finite coefficient as new features of proteins. And the basic classifier is weighted K-nearest neighbor. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 83.3% for SNL6 and 87.6 % for SNL9. By comparing its results with other methods, it indicates the new approach is feasible and effective.

scholarly journals Implementasi Data Mining untuk Deteksi Penyakit Ginjal Kronis (PGK) menggunakan K-Nearest Neighbor (KNN) dengan Backward Elimination

2020 ◽  
Vol 7 (2) ◽  
pp. 417
Author(s):  
Ikhsan Wisnuadji Gamadarenda ◽  
Indra Waspada
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Data Discovery ◽  
Nearest Neighbor Algorithm ◽  
Web Based ◽  
Elimination Algorithm ◽  
Backward Elimination ◽  
K Nearest Neighbor Algorithm

<p class="Abstrak">Penyakit ginjal kronis (PGK) merupakan masalah kesehatan publik di seluruh dunia dengan insiden yang terus meningkat. Berdasarkan sumber dari BPJS Kesehatan, perawatan PGK merupakan ranking kedua pembiayaan terbesar setelah penyakit jantung. Pendeteksian PGK juga memerlukan banyak atribut sehingga membutuhkan biaya yang cukup mahal. Oleh sebab itu dibuat sistem dengan tahapan data mining berbasis web yang memudahkan untuk melakukan deteksi PGK, sehingga PGK dapat dicegah, ditanggulangi, dan kemungkinan mendapatkan terapi yang efektif lebih besar jika diketahui lebih awal. Proses penelitian ini menggunakan sebuah rangka kerja<em> data mining</em> <em>Knowledge Data Discover</em>y (KDD). Dalam skenario rangka kerja yang digunakan, sistem ini menggunakan Algoritme <em>Backward Elimination</em> untuk mengurangi jumlah atribut yang dipakai dengan tujuan untuk mengurangi jenis pemeriksaan yang dilakukan, dan Algoritme k-<em>Nearest Neighbor</em> sebagai algoritme klasifikasi untuk mendeteksi penyakit. Hasil pemodelan terbaik <em>data mining</em> dari sistem yang dibuat menggunakan <em>Backward Elimination</em> (α = 0,05) dan kNN (<em>k = </em>3) dengan pertimbangan penurunan biaya pemeriksaan dan sensitivity tertinggi. Rekomendasi sistem menghasilkan 10 atribut yang terpilih dari 24 atribut awal yang digunakan, yaitu: berat jenis (<em>sg</em>), albumin (<em>al</em>), urea darah (<em>bu</em>), kreatinin serum (<em>sc</em>), sodium (<em>sod</em>), hemoglobin (<em>hemo</em>), sel darah merah (<em>rbc</em>), hipertensi (<em>htn</em>), diabetes mellitus (<em>dm</em>), dan nafsu makan (<em>appet</em>). Penggunaan atribut yang telah terseleksi tersebut, berhasil menekan biaya pemeriksaan hingga 73,36%. Selanjutnya dilakukan pendeteksian penyakit menggunakan Algoritme k-<em>Nearest Neighbor </em>menghasilkan nilai akurasi sebesar 99,25%, <em>sensitivity</em> sebesar 99,5%, dan <em>specificity</em> sebesar 98,745%.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Chronic kidney disease (CKD) is a health problem for people around the world with increasing incidence. Based on sources from BPJS Kesehatan, CKD care is the second largest ranking of financing after heart disease. CKD detection also requires many attributes, so it requires quite expensive costs. Create a system with web-based data mining stages that makes it easy to detect CKD. Allowing CKD to be prevented, addressed, and advised to get effective therapy is greater if acknowledged earlier. The process of this research uses work methods of Data Mining Knowledge Data Discovery (KDD). In the framework of the framework used, this system uses the Backward Elimination Algorithm to reduce the number of attributes used to reduce the type of inspection performed, and the k-Nearest Neighbor Algorithm as an algorithm to update disease. The best data mining modeling results from the system are made using Backward Elimination (α = 0.05) and kNN (k = 3) by calculating the increase in inspection costs and the highest sensitivity. System recommendations produce 10 attributes selected from the 24 initial attributes used, namely: specific gravity (sg), albumin (al), blood urea (bu), serum creatinine (sc), sodium (soil), hemoglobin (hemo), cell red blood (rbc), hypertension (htn), diabetes mellitus (dm), and appetite (appetite). The use of the selected attributes succeeded in achieving inspection costs of up to 73.36%. Furthermore, disease detection using the k-Nearest Neighbor Algorithm produces an accuracy value of 99.25%, sensitivity of 99.5%, and specificity of 98.745%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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