Bagging Nearest-Neighbor Prediction independence Test: an efficient method for nonlinear dependence of two continuous variables

We apply the Ising model with nearest-neighbor correlations (INNC) in the problem of interpolation of spatially correlated data on regular grids. The correlations are captured by short-range interactions between “Ising spins”. The INNC algorithm can be used with label data (classification) as well as discrete and continuous real-valued data (regression). In the regression problem, INNC approximates continuous variables by means of a user-specified number of classes. INNC predicts the class identity at unmeasured points by using the Monte Carlo simulation conditioned on the observed data (partial sample). The algorithm locally respects the sample values and globally aims to minimize the deviation between an energy measure of the partial sample and that of the entire grid. INNC is non-parametric and, thus, is suitable for non-Gaussian data. The method is found to be very competitive with respect to interpolation accuracy and computational efficiency compared to some standard methods. Thus, this method provides a useful tool for filling gaps in gridded data such as satellite images.

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Efficiency of Foreign Exchange Markets in Sub-Saharan Africa in the Presence of Structural Break: A Linear and Non-Linear Testing Approach

Journal of Economics and Behavioral Studies ◽

10.22610/jebs.v9i4.1827 ◽

2017 ◽

Vol 9 (4) ◽

pp. 122

Author(s):

Adeyeye Patrick Olufemi ◽

Aluko Olufemi Adewale ◽

Migiro Stephen Oseko

Keyword(s):

Foreign Exchange ◽

Structural Break ◽

Sub Saharan Africa ◽

Nonlinear Dependence ◽

Ratio Test ◽

Variance Ratio ◽

Wild Bootstrap ◽

Independence Test ◽

Variance Ratio Test ◽

Sub Saharan

This study examines the efficiency of foreign exchange (forex) market of 10 selected countries in sub-Saharan Africa in the presence of structural break. It uses data on the average official exchange rate of currencies of the selected countries to the US dollar from November 1995 to October 2015. This study employs Perron unit root test with structural break to endogenously determine the break period in the forex markets. It also employs the Kim wild bootstrap variance ratio test and BDS independence test to detect linear and nonlinear dependence in forex market returns respectively. In the full sample period, the Kim wild bootstrap joint variance ratio test shows that only two forex markets are efficient while the BDS independence test reports that all the forex markets are not efficient. The subsample period analysis indicates that the efficiency of the majority of the forex markets is sensitive to structural break, thus providing evidence in support of the adaptive market hypothesis. This study suggests that ignoring structural break and nonlinearity of returns may lead to misleading results when testing for market efficiency.

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Efficient test for nonlinear dependence of two continuous variables

BMC Bioinformatics ◽

10.1186/s12859-015-0697-7 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 13

Author(s):

Yi Wang ◽

Yi Li ◽

Hongbao Cao ◽

Momiao Xiong ◽

Yin Yao Shugart ◽

...

Keyword(s):

Nonlinear Dependence ◽

Continuous Variables ◽

Efficient Test

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Efisiensi Big Data Menggunakan Improved Nearest Neighbor

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2019662085 ◽

2019 ◽

Vol 6 (6) ◽

pp. 665

Author(s):

Aditya Hari Bawono ◽

Ahmad Afif Supianto

Keyword(s):

Data Mining ◽

Big Data ◽

Efficient Method ◽

Nearest Neighbor ◽

Classification Methods ◽

K Nearest Neighbor ◽

Knn Classification ◽

Degree Of Similarity ◽

Selection Of

Klasifikasi adalah salah satu metode penting dalam kajian data mining. Salah satu metode klasifikasi yang populer dan mendasar adalah k-nearest neighbor (kNN). Pada kNN, hubungan antar sampel diukur berdasarkan tingkat kesamaan yang direpresentasikan sebagai jarak. Pada kasus mayoritas terutama pada data berukuran besar, akan terdapat beberapa sampel yang memiliki jarak yang sama namun amat mungkin tidak terpilih menjadi tetangga, maka pemilihan parameter k akan sangat mempengaruhi hasil klasifikasi kNN. Selain itu, pengurutan pada kNN menjadi masalah komputasi ketika dilakukan pada data berukuran besar. Dalam usaha mengatasi klasifikasi data berukuran besar dibutuhkan metode yang lebih akurat dan efisien. Dependent Nearest Neighbor (dNN) sebagai metode yang diajukan dalam penelitian ini tidak menggunakan parameter k dan tidak ada proses pengurutan sampel. Hasil percobaan menunjukkan bahwa dNN dapat menghasilkan efisiensi waktu sebesar 3 kali lipat lebih cepat daripada kNN. Perbandingan akurasi dNN adalah 13% lebih baik daripada kNN.AbstractClassification is one of the important methods of data mining. One of the most popular and basic classification methods is k-nearest neighbor (kNN). In kNN, the relationships between samples are measured by the degree of similarity represented as distance. In major cases, especially on big data, there will be some samples that have the same distance but may not be selected as neighbors, then the selection of k parameters will greatly affect the results of kNN classification. Sorting phase of kNN becomes a computation problem when it is done on big data. In the effort to overcome the classification of big data a more accurate and efficient method is required. Dependent Nearest Neighbor (dNN) as method proposed in this study did not use the k parameters and no sample at the sorting phase. The proposed method resulted in 3 times faster than kNN. The accuracy of the proposed method is13% better results than kNN.

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Quantum discord and quantum phase transition in spin-1/2 frustrated Heisenberg chain

Quantum Information and Computation ◽

10.26421/qic13.5-6-6 ◽

2013 ◽

Vol 13 (5&6) ◽

pp. 452-468

Author(s):

Chu-Hui Fan ◽

Heng-Na Xiong ◽

Yixiao Huang ◽

Zhe Sun

Keyword(s):

Phase Transition ◽

Quantum Phase Transition ◽

Quantum Discord ◽

Nearest Neighbor ◽

Finite Size ◽

Quantum Phase ◽

Nonlinear Dependence ◽

Heisenberg Chain ◽

Transition Points ◽

Analytical Result

By using the concept of the quantum discord (QD), we study the spin-1/2 antiferromagnetic Heisenberg chain with next-nearest-neighbor interaction. Due to the $SU(2)$ symmetry and $Z_{2}$ symmetry in this system, we obtain the analytical result of the QD and its geometric measure (GMQD), which is determined by the two-site correlators. For the 4-site and 6-site cases, the connection between GMQD (QD) and the eigenenergies was revealed. From the analytical and numerical results, we find GMQD (QD) is an effective tool in detecting the both the first-order and the infinite-order quantum-phase-transition points for the finite-size systems. Moreover, by using the entanglement excitation energy and a universal frustration measure we consider the frustration properties of the system and find a nonlinear dependence of the GMQD on the frustration.

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Efficient method for maximizing bichromatic reverse nearest neighbor

Proceedings of the VLDB Endowment ◽

10.14778/1687627.1687754 ◽

2009 ◽

Vol 2 (1) ◽

pp. 1126-1137 ◽

Cited By ~ 58

Author(s):

Raymond Chi-Wing Wong ◽

M. Tamer Özsu ◽

Philip S. Yu ◽

Ada Wai-Chee Fu ◽

Lian Liu

Keyword(s):

Efficient Method ◽

Nearest Neighbor ◽

Reverse Nearest Neighbor

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An Efficient Method for Similarity Measure in Independent PCA based Classification

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v9i3.3335 ◽

2013 ◽

Vol 9 (3) ◽

pp. 1099-1109

Author(s):

Dr. H. B. Kekre ◽

Dr. Tanuja K. Sarode ◽

Jagruti K. Save

Keyword(s):

Coordinate System ◽

Similarity Measure ◽

Efficient Method ◽

Nearest Neighbor ◽

Feature Vector ◽

Test Image ◽

Nearest Neighbor Classifier ◽

Feature Vectors ◽

The Given ◽

Neighbor Classifier

The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared withÂ distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.

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Efficiency of Foreign Exchange Markets in Sub-Saharan Africa in the Presence of Structural Break: A Linear and Non-Linear Testing Approach

Journal of Economics and Behavioral Studies ◽

10.22610/jebs.v9i4(j).1827 ◽

2017 ◽

Vol 9 (4(J)) ◽

pp. 122-131

Author(s):

Adeyeye Patrick Olufemi ◽

Aluko Olufemi Adewale ◽

Migiro Stephen Oseko

Keyword(s):

Foreign Exchange ◽

Structural Break ◽

Sub Saharan Africa ◽

Nonlinear Dependence ◽

Ratio Test ◽

Variance Ratio ◽

Wild Bootstrap ◽

Independence Test ◽

Variance Ratio Test ◽

Sub Saharan

This study examines the efficiency of foreign exchange (forex) market of 10 selected countries in sub-Saharan Africa in the presence of structural break. It uses data on the average official exchange rate of currencies of the selected countries to the US dollar from November 1995 to October 2015. This study employs Perron unit root test with structural break to endogenously determine the break period in the forex markets. It also employs the Kim wild bootstrap variance ratio test and BDS independence test to detect linear and nonlinear dependence in forex market returns respectively. In the full sample period, the Kim wild bootstrap joint variance ratio test shows that only two forex markets are efficient while the BDS independence test reports that all the forex markets are not efficient. The subsample period analysis indicates that the efficiency of the majority of the forex markets is sensitive to structural break, thus providing evidence in support of the adaptive market hypothesis. This study suggests that ignoring structural break and nonlinearity of returns may lead to misleading results when testing for market efficiency.

Download Full-text

Efisiensi Big Data Menggunakan Improved Nearest Neighbor

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020712085 ◽

2019 ◽

Vol 6 (6) ◽

pp. 665

Author(s):

Aditya Hari Bawono ◽

Ahmad Afif Supianto

Keyword(s):

Data Mining ◽

Big Data ◽

Efficient Method ◽

Nearest Neighbor ◽

Classification Methods ◽

K Nearest Neighbor ◽

Knn Classification ◽

Degree Of Similarity ◽

Selection Of

Klasifikasi adalah salah satu metode penting dalam kajian data mining. Salah satu metode klasifikasi yang populer dan mendasar adalah k-nearest neighbor (kNN). Pada kNN, hubungan antar sampel diukur berdasarkan tingkat kesamaan yang direpresentasikan sebagai jarak. Pada kasus mayoritas terutama pada data berukuran besar, akan terdapat beberapa sampel yang memiliki jarak yang sama namun amat mungkin tidak terpilih menjadi tetangga, maka pemilihan parameter k akan sangat mempengaruhi hasil klasifikasi kNN. Selain itu, pengurutan pada kNN menjadi masalah komputasi ketika dilakukan pada data berukuran besar. Dalam usaha mengatasi klasifikasi data berukuran besar dibutuhkan metode yang lebih akurat dan efisien. Dependent Nearest Neighbor (dNN) sebagai metode yang diajukan dalam penelitian ini tidak menggunakan parameter k dan tidak ada proses pengurutan sampel. Hasil percobaan menunjukkan bahwa dNN dapat menghasilkan efisiensi waktu sebesar 3 kali lipat lebih cepat daripada kNN. Perbandingan akurasi dNN adalah 13% lebih baik daripada kNN.AbstractClassification is one of the important methods of data mining. One of the most popular and basic classification methods is k-nearest neighbor (kNN). In kNN, the relationships between samples are measured by the degree of similarity represented as distance. In major cases, especially on big data, there will be some samples that have the same distance but may not be selected as neighbors, then the selection of k parameters will greatly affect the results of kNN classification. Sorting phase of kNN becomes a computation problem when it is done on big data. In the effort to overcome the classification of big data a more accurate and efficient method is required. Dependent Nearest Neighbor (dNN) as method proposed in this study did not use the k parameters and no sample at the sorting phase. The proposed method resulted in 3 times faster than kNN. The accuracy of the proposed method is13% better results than kNN.

Download Full-text