Multi-label classification of Indonesian news topics using Pseudo Nearest Neighbor Rule

The purpose of this study is twofold: (1) to develop a classification method to distinguish between asymptomatic (AS) and knee osteoarthritis (OA) gait patterns using ground reaction force (GRF) measurements, and (2) to investigate OA severity within OA gait patterns. Features were first extracted from the GRF vectors to be used for classification. We investigated a two-level hierarchical classification and analysis method using the nearest neighbor rule. At the first level, the GRF data were classified into two classes: AS and OA. At the second level, the GRF data of OA patients were classified according to the pathology severity. The OA patients were grouped into two OA severity categories according to the Kellgren and Lawrence (KL) scale: KL 1 and KL 2 for one category, and KL 3 and KL 4 for the other. Experiments were conducted using data of 42 cases, 16 AS and 26 pathological. The method discriminated between AS and OA subjects with an accuracy of 38 of 42 cases, and assessed the severity correctly with an accuracy of 20 of 26 cases. These results demonstrated the validity of both, the feature and the classifier, for automatic classification of AS and knee OA gait patterns and for analysis of OA severity.

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A Kullback Leibler-nearest neighbor rule classification of EEGs: The EEG population screening problem, an anesthesia level EEG classification application

Computers and Biomedical Research ◽

10.1016/0010-4809(80)90022-1 ◽

1980 ◽

Vol 13 (3) ◽

pp. 283-296 ◽

Cited By ~ 3

Author(s):

Will Gersch ◽

F. Martinelli ◽

J. Yonemoto ◽

M.D. Low ◽

J.A. McEwen

Keyword(s):

Nearest Neighbor ◽

Population Screening ◽

Eeg Classification ◽

Nearest Neighbor Rule ◽

Anesthesia Level

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Classification of multiple observations using multi-stage rank nearest neighbor rule

Journal of Statistical Planning and Inference ◽

10.1016/s0378-3758(98)00137-2 ◽

1999 ◽

Vol 76 (1-2) ◽

pp. 163-183 ◽

Cited By ~ 6

Author(s):

S.C. Bagui ◽

K.L. Mehra

Keyword(s):

Nearest Neighbor ◽

Multiple Observations ◽

Multi Stage ◽

Nearest Neighbor Rule

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Classification of multiple observations using a rank nearest neighbor rule

Pattern Recognition Letters ◽

10.1016/0167-8655(93)90045-f ◽

1993 ◽

Vol 14 (8) ◽

pp. 611-617

Author(s):

S.C. Bagui

Keyword(s):

Nearest Neighbor ◽

Multiple Observations ◽

Nearest Neighbor Rule

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Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves

Pattern Recognition Applications in Engineering - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-1839-7.ch008 ◽

2020 ◽

pp. 180-203

Author(s):

Mauricio Orozco-Alzate

Keyword(s):

Nearest Neighbor ◽

Computational Cost ◽

Similarity Measures ◽

Classification Performance ◽

Accurate Identification ◽

Distribution Matching ◽

Effective Option ◽

Recent Developments ◽

Nearest Neighbor Rule

The accurate identification of plant species is crucial in botanical taxonomy as well as in related fields such as ecology and biodiversity monitoring. In spite of the recent developments in DNA-based analyses for phylogeny and systematics, visual leaf recognition is still commonly applied for species identification in botany. Histograms, along with the well-known nearest neighbor rule, are often a simple but effective option for the representation and classification of leaf images. Such an option relies on the choice of a proper dissimilarity measure to compare histograms. Two state-of-the-art measures—called weighted distribution matching (WDM) and Poisson-binomial radius (PBR)—are compared here in terms of classification performance, computational cost, and non-metric/non-Euclidean behavior. They are also compared against other classical dissimilarity measures between histograms. Even though PBR gives the best performance at the highest cost, it is not significantly better than other classical measures. Non-Euclidean/non-metric nature seems to play an important role.

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Machine Learning Verdict of EEG Signals in Brain Computer Interface

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1838114 ◽

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.

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Hydroponic Nutrient Control System Based on Internet of Things

CommIT (Communication and Information Technology) Journal ◽

10.21512/commit.v13i2.6016 ◽

2019 ◽

Vol 13 (2) ◽

Cited By ~ 1

Author(s):

Herman Herman ◽

Demi Adidrana ◽

Nico Surantha ◽

Suharjito Suharjito

Keyword(s):

Urban Areas ◽

Human Population ◽

Mineral Water ◽

Nearest Neighbor ◽

Total Dissolved Solids ◽

K Nearest Neighbor ◽

Turn On ◽

Nutrient Film Technique ◽

Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

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