34 research outputs found
Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm
Hyperspectral images (HIS) classification is a high technical remote sensing
tool. The goal is to reproduce a thematic map that will be compared with a
reference ground truth map (GT), constructed by expecting the region. The HIS
contains more than a hundred bidirectional measures, called bands (or simply
images), of the same region. They are taken at juxtaposed frequencies.
Unfortunately, some bands contain redundant information, others are affected by
the noise, and the high dimensionality of features made the accuracy of
classification lower. The problematic is how to find the good bands to classify
the pixels of regions. Some methods use Mutual Information (MI) and threshold,
to select relevant bands, without treatment of redundancy. Others control and
eliminate redundancy by selecting the band top ranking the MI, and if its
neighbors have sensibly the same MI with the GT, they will be considered
redundant and so discarded. This is the most inconvenient of this method,
because this avoids the advantage of hyperspectral images: some precious
information can be discarded. In this paper we'll accept the useful redundancy.
A band contains useful redundancy if it contributes to produce an estimated
reference map that has higher MI with the GT.nTo control redundancy, we
introduce a complementary threshold added to last value of MI. This process is
a Filter strategy; it gets a better performance of classification accuracy and
not expensive, but less preferment than Wrapper strategy.Comment: 11 pages, 5 figures, journal pape
KLASYFIKACJA CHOROBY PARKINSONA I INNYCH ZABURZE艃 NEUROLOGICZNYCH Z WYKORZYSTANIEM EKSTRAKCJI CECH G艁OSOWYCH I TECHNIK REDUKCJI
This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications.Przedstawione badanie mia艂o na celu r贸偶nicowanie os贸b z chorob膮 Parkinsona (PD) od os贸b z innymi zaburzeniami neurologicznymi poprzez analiz臋 pr贸bek g艂osowych, bior膮c pod uwag臋 zwi膮zek mi臋dzy zaburzeniami g艂osu a PD. Pr贸bki g艂osowe zosta艂y zebrane od 76 uczestnik贸w przy u偶yciu r贸偶nych urz膮dze艅 i warunk贸w nagrywania, a uczestnicy byli instruowani, aby wyd艂u偶y膰 samog艂osk臋 /a/ w wygodnym tempie. Oprogramowanie PRAAT zosta艂o zastosowane do ekstrakcji cech, takich jak autokorelacja (AC), krzy偶owa korelacja (CC) i wsp贸艂czynniki cepstralne Mel (MFCC) z pr贸bek g艂osowych. Analiza sk艂adowych g艂贸wnych (PCA) zosta艂a wykorzystana w celu zmniejszenia wymiarowo艣ci cech. Jako techniki nadzorowanego uczenia maszynowego wykorzystano drzewa decyzyjne (CT), regresj臋 logistyczn膮, naiwny klasyfikator Bayesa (NB), maszyny wektor贸w no艣nych (SVM) oraz metody zespo艂owe. Ka偶da z tych metod posiada艂a swoje unikalne mocne strony i charakterystyki, umo偶liwiaj膮c kompleksow膮 ocen臋 ich skuteczno艣ci w rozr贸偶nianiu pacjent贸w z PD od os贸b z innymi zaburzeniami neurologicznymi. Naiwny klasyfikator Bayesa, wykorzystuj膮cy siedem sk艂adowych PCA, osi膮gn膮艂 najwy偶szy wska藕nik dok艂adno艣ci na poziomie 86,84% w艣r贸d przetestowanych metod klasyfikacji. Nale偶y jednak zauwa偶y膰, 偶e wydajno艣膰 klasyfikatora mo偶e si臋 r贸偶ni膰 w zale偶no艣ci od zbioru danych i konkretnych cech pr贸bek g艂osowych. Podsumowuj膮c, to badanie wykaza艂o potencja艂 analizy g艂osu jako narz臋dzia diagnostycznego do rozr贸偶niania pacjent贸w z PD od os贸b z innymi zaburzeniami neurologicznymi. Poprzez zastosowanie r贸偶nych technik analizy g艂osu i wykorzystanie r贸偶nych algorytm贸w uczenia maszynowego, takich jak drzewa decyzyjne, regresja logistyczna, naiwny klasyfikator Bayesa, maszyny wektor贸w no艣nych i metody zespo艂owe, osi膮gni臋to znacz膮cy poziom dok艂adno艣ci. Niemniej jednak, konieczne s膮 dalsze badania i walidacja na wi臋kszych zbiorach danych w celu skonsolidowania i uog贸lnienia tych wynik贸w dla przysz艂ych zastosowa艅 klinicznych
KOMPLEKSOWE METODY UCZENIA MASZYNOWEGO I UCZENIA G艁臉BOKIEGO DO KLASYFIKACJI CHOROBY PARKINSONA I OCENY JEJ NASILENIA
In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed reports to facilitate a comprehensive comparative analysis of these models. Notably, XGBoost demonstrated the highest precision at 97.4%. Additionally, we took a step further by developing a Gated Recurrent Unit (GRU) model with the purpose of combining predictions from alternative models. We assessed its ability to predict the severity of the ailment. To quantify the precision levels of the models in disease classification, we calculated severity percentages. Furthermore, we created a Receiver Operating Characteristic (ROC) curve for the GRU model, simplifying the evaluation of its capability to distinguish among various severity levels. This comprehensive approach contributes to a more accurate and detailed understanding of Parkinson's disease severity assessment.W tym badaniu naszym celem by艂o przyj臋cie kompleksowego podej艣cia do kategoryzacji i oceny ci臋偶ko艣ci choroby Parkinsona poprzez wykorzystanie technik zar贸wno uczenia maszynowego, jak i g艂臋bokiego uczenia. Dok艂adnie ocenili艣my skuteczno艣膰 r贸偶nych modeli, w tym XGBoost, Random Forest, Multi-Layer Perceptron (MLP) i Recurrent Neural Network (RNN), wykorzystuj膮c wska藕niki klasyfikacji. Wygenerowali艣my szczeg贸艂owe raporty, aby u艂atwi膰 kompleksow膮 analiz臋 por贸wnawcz膮 tych modeli. Warto zauwa偶y膰, 偶e XGBoost wykaza艂 najwy偶sz膮 precyzj臋 na poziomie 97,4%. Ponadto poszli艣my o krok dalej, opracowuj膮c model Gated Recurrent Unit (GRU) w celu po艂膮czenia przewidywa艅 z alternatywnych modeli. Ocenili艣my jego zdolno艣膰 do przewidywania nasilenia dolegliwo艣ci. Aby okre艣li膰 ilo艣ciowo poziomy dok艂adno艣ci modeli w klasyfikacji chor贸b, obliczyli艣my warto艣ci procentowe nasilenia. Ponadto stworzyli艣my krzyw膮 charakterystyki operacyjnej odbiornika (ROC) dla modelu GRU, upraszczaj膮c ocen臋 jego zdolno艣ci do rozr贸偶niania r贸偶nych poziom贸w nasilenia. To kompleksowe podej艣cie przyczynia si臋 do dok艂adniejszego i bardziej szczeg贸艂owego zrozumienia oceny ci臋偶ko艣ci choroby Parkinsona
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
Band selection is a great challenging task in the classification of
hyperspectral remotely sensed images HSI. This is resulting from its high
spectral resolution, the many class outputs and the limited number of training
samples. For this purpose, this paper introduces a new filter approach for
dimension reduction and classification of hyperspectral images using
information theoretic (normalized mutual information) and support vector
machines SVM. This method consists to select a minimal subset of the most
informative and relevant bands from the input datasets for better
classification efficiency. We applied our proposed algorithm on two well-known
benchmark datasets gathered by the NASA's AVIRIS sensor over Indiana and
Salinas valley in USA. The experimental results were assessed based on
different evaluation metrics widely used in this area. The comparison with the
state of the art methods proves that our method could produce good performance
with reduced number of selected bands in a good timing.
Keywords: Dimension reduction, Hyperspectral images, Band selection,
Normalized mutual information, Classification, Support vector machinesComment: http://www.scopus.com/inward/record.url?eid=2-s2.0-85056469155&partnerID=MN8TOAR
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information
Nowadays, the hyperspectral remote sensing imagery HSI becomes an important
tool to observe the Earth's surface, detect the climatic changes and many other
applications. The classification of HSI is one of the most challenging tasks
due to the large amount of spectral information and the presence of redundant
and irrelevant bands. Although great progresses have been made on
classification techniques, few studies have been done to provide practical
guidelines to determine the appropriate classifier for HSI. In this paper, we
investigate the performance of four supervised learning algorithms, namely,
Support Vector Machines SVM, Random Forest RF, K-Nearest Neighbors KNN and
Linear Discriminant Analysis LDA with different kernels in terms of
classification accuracies. The experiments have been performed on three real
hyperspectral datasets taken from the NASA's Airborne Visible/Infrared Imaging
Spectrometer Sensor AVIRIS and the Reflective Optics System Imaging
Spectrometer ROSIS sensors. The mutual information had been used to reduce the
dimensionality of the used datasets for better classification efficiency. The
extensive experiments demonstrate that the SVM classifier with RBF kernel and
RF produced statistically better results and seems to be respectively the more
suitable as supervised classifiers for the hyperspectral remote sensing images.
Keywords: hyperspectral images, mutual information, dimension reduction,
Support Vector Machines, K-Nearest Neighbors, Random Forest, Linear
Discriminant Analysis
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier
Over the past decades, the hyperspectral remote sensing technology
development has attracted growing interest among scientists in various domains.
The rich and detailed spectral information provided by the hyperspectral
sensors has improved the monitoring and detection capabilities of the earth
surface substances. However, the high dimensionality of the hyperspectral
images (HSI) is one of the main challenges for the analysis of the collected
data. The existence of noisy, redundant and irrelevant bands increases the
computational complexity, induce the Hughes phenomenon and decrease the
target's classification accuracy. Hence, the dimensionality reduction is an
essential step to face the dimensionality challenges. In this paper, we propose
a novel filter approach based on the maximization of the spectral interaction
measure and the support vector machines for dimensionality reduction and
classification of the HSI. The proposed Max Relevance Max Synergy (MRMS)
algorithm evaluates the relevance of every band through the combination of
spectral synergy, redundancy and relevance measures. Our objective is to select
the optimal subset of synergistic bands providing accurate classification of
the supervised scene materials. Experimental results have been performed using
three different hyperspectral datasets: "Indiana Pine", "Pavia University" and
"Salinas" provided by the "NASA-AVIRIS" and the "ROSIS" spectrometers.
Furthermore, a comparison with the state of the art band selection methods has
been carried out in order to demonstrate the robustness and efficiency of the
proposed approach.
Keywords: Hyperspectral images, remote sensing, dimensionality reduction,
classification, synergic, correlation, spectral interaction information, mutual
infor
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
Recently, the hyperspectral sensors have improved our ability to monitor the
earth surface with high spectral resolution. However, the high dimensionality
of spectral data brings challenges for the image processing. Consequently, the
dimensionality reduction is a necessary step in order to reduce the
computational complexity and increase the classification accuracy. In this
paper, we propose a new filter approach based on information gain for
dimensionality reduction and classification of hyperspectral images. A special
strategy based on hyperspectral bands selection is adopted to pick the most
informative bands and discard the irrelevant and noisy ones. The algorithm
evaluates the relevancy of the bands based on the information gain function
with the support vector machine classifier. The proposed method is compared
using two benchmark hyperspectral datasets (Indiana, Pavia) with three
competing methods. The comparison results showed that the information gain
filter approach outperforms the other methods on the tested datasets and could
significantly reduce the computation cost while improving the classification
accuracy. Keywords: Hyperspectral images; dimensionality reduction; information
gain; classification accuracy.
Keywords: Hyperspectral images; dimensionality reduction; information gain;
classification accuracy
CLASSIFICATION OF PARKINSON鈥橲 DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES
This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications