Academic literature on the topic 'LDA+KNN CLASSIFIER'
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Journal articles on the topic "LDA+KNN CLASSIFIER"
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McHUGH, E. S., A. P. SHINN, and J. W. KAY. "Discrimination of the notifiable pathogen Gyrodactylus salaris from G. thymalli (Monogenea) using statistical classifiers applied to morphometric data." Parasitology 121, no. 3 (September 2000): 315–23. http://dx.doi.org/10.1017/s0031182099006381.
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The identification and discrimination of 2 closely related and morphologically similar species of Gyrodactylus, G. salaris and G. thymalli, were assessed using the statistical classification methodologies Linear Discriminant Analysis (LDA) and k-Nearest Neighbours (KNN). These statistical methods were applied to morphometric measurements made on the gyrodactylid attachment hooks. The mean estimated classification percentages of correctly identifying each species were 98·1% (LDA) and 97·9% (KNN) for G. salaris and 99·9% (LDA) and 73·2% (KNN) for G. thymalli. The analysis was expanded to include another 2 closely related species and the new classification efficiencies were 94·6% (LDA) and 98·0% (KNN) for G. salaris; 98·2% (LDA) and 72·6% (KNN) for G. thymalli; 86·7% (LDA) and 91·8% (KNN) for G. derjavini; and 76·5% (LDA) and 77·7% (KNN) for G. truttae. The higher correct classification scores of G. salaris and G. thymalli by the LDA classifier in the 2-species analysis over the 4-species analysis suggested the development of a 2-stage classifier. The mean estimated correct classification scores were 99·97% (LDA) and 99·99% (KNN) for the G. salaris–G. thymalli pairing and 99·4% (LDA) and 99·92% (KNN) for the G. derjavini–G. truttae pairing. Assessment of the 2-stage classifier using only marginal hook data was very good with classification efficiencies of 100% (LDA) and 99·6% (KNN) for the G. salaris–G. thymalli pairing and 97·2% (LDA) and 99·2% (KNN) for the G. derjavini–G. truttae pairing. Paired species were then discriminated individually in the second stage of the classifier using data from the full set of hooks. These analyses demonstrate that using the methods of LDA and KNN statistical classification, the discrimination of closely related and pathogenic species of Gyrodactylus may be achieved using data derived from light microscope studies.
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Khan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam, and Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier." Diagnostics 12, no. 11 (October 26, 2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.
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Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called “Stacking Classifier” in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
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R. Essa, Raghad, Hanadi Abbas Jaber, and Abbas A. Jasim. "Features selection for estimating hand gestures based on electromyography signals." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2087–94. http://dx.doi.org/10.11591/beei.v12i4.5048.
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Hand prosthesis controlled by surface electromyography (sEMG) is promising due to the control capabilities and the noninvasive technique that machine learning (ML) offers to help physically disabled people during daily life. Nevertheless, dexterous prostheses are still infrequently popular due to control problems and limited robustness. This paper proposes a new set of time domain (TD) features to improve the EMG pattern recognition performance. The effect of five feature sets is evaluated based on the three classifiers k-nearest neighbor (KNN), linear discriminate analysis (LDA), and support vector machine (SVM). The EMG signals are obtained from database-5 (DB5) of the ninapro project datasets. In this study, the long-term signals of DB5 are segmented into short-term signals to perform short-term recognition. The results showed that the LDA classifier based on the proposed features achieved high classification accuracy for classifing 17 gestures. The LDA classifier achieved about 96.47% compared to 94.12%, and 93.82% for KNN and SVM classifiers, respectively. The results confirm that the suitable features extracted from short term signals with the appropriate classifier, has an important impact on improving the performance of gesture classification.
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R. Essa, Raghad, Hanadi Abbas Jaber, and Abbas A. Jasim. "Features selection for estimating hand gestures based on electromyography signals." Bulletin of Electrical Engineering and Informatics 12, no. 4 (August 1, 2023): 2087–94. http://dx.doi.org/10.11591/eei.v12i4.5048.
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Hand prosthesis controlled by surface electromyography (sEMG) is promising due to the control capabilities and the noninvasive technique that machine learning (ML) offers to help physically disabled people during daily life. Nevertheless, dexterous prostheses are still infrequently popular due to control problems and limited robustness. This paper proposes a new set of time domain (TD) features to improve the EMG pattern recognition performance. The effect of five feature sets is evaluated based on the three classifiers k-nearest neighbor (KNN), linear discriminate analysis (LDA), and support vector machine (SVM). The EMG signals are obtained from database-5 (DB5) of the ninapro project datasets. In this study, the long-term signals of DB5 are segmented into short-term signals to perform short-term recognition. The results showed that the LDA classifier based on the proposed features achieved high classification accuracy for classifing 17 gestures. The LDA classifier achieved about 96.47% compared to 94.12%, and 93.82% for KNN and SVM classifiers, respectively. The results confirm that the suitable features extracted from short term signals with the appropriate classifier, has an important impact on improving the performance of gesture classification.
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Zou, Xiuguo, Chenyang Wang, Manman Luo, Qiaomu Ren, Yingying Liu, Shikai Zhang, Yungang Bai, Jiawei Meng, Wentian Zhang, and Steven W. Su. "Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine." Sensors 22, no. 8 (April 14, 2022): 2997. http://dx.doi.org/10.3390/s22082997.
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Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA–KNN-SVM classifier was 96.45%, and the LDA–KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.
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Bilucaglia, Marco, Gian Marco Duma, Giovanni Mento, Luca Semenzato, and Patrizio E. Tressoldi. "Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity." F1000Research 9 (October 13, 2021): 173. http://dx.doi.org/10.12688/f1000research.22202.3.
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Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.
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Bilucaglia, Marco, Gian Marco Duma, Giovanni Mento, Luca Semenzato, and Patrizio E. Tressoldi. "Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity." F1000Research 9 (October 8, 2021): 173. http://dx.doi.org/10.12688/f1000research.22202.2.
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Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.
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P, Sunil Kumar, and Harikumar Rajaguru. "ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K-NEAREST NEIGHBORS MEASURES." Asian Journal of Pharmaceutical and Clinical Research 9, no. 5 (September 1, 2016): 91. http://dx.doi.org/10.22159/ajpcr.2016.v9i5.12196.
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ABSTRACTObjective: The main aim of this research is to reduce the dimension of the epileptic Electroencephalography (EEG) signals and then classify it usingvarious post classifiers. For the evaluation and easy treatment of neurological diseases, EEG signals are used. The reflection of the electrical activitiesof the human brain is obtained by the measurement of potentials in EEG. To study and explore the brain functions in an exhaustive manner, EEG is usedby both physicians and scientists. The study of the electrical activity of the brain which is done through EEG recording is a vital tool for the diagnosis ofmany neurological diseases which include epilepsy, sleep disorders, injuries in head, dementia etc. One of the most commonly occurring and prevalentneurological disorders is epilepsy and it is easily characterized by recurrent seizures.Methods: This paper employs the concept of dimensionality reduction concepts like Fuzzy Mutual Information (FMI), Independent ComponentAnalysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and finally Variational Bayesian Matrix Factorization (VBMF).The epilepsy risk levels are also classified using post classifiers like Singular Value Decomposition (SVD), Approximate Entropy (ApEn) and WeightedKNN (W-KNN) classifiers.Results: The highest accuracy is obtained when LDA is combined with Weighted KNN (W-KNN) Classifiers and it is of 97.18%. Conclusion: Thus the EEG signals not only represent the brain function but also the status of the whole body. The best result obtained was whenLDA is engaged as a dimensionality reduction technique followed by the usage of the W-KNN as post classifier for the classification of epilepsy risklevels from EEG signals. Future work may incorporate the possible usage of different dimensionality reduction techniques with various other types ofclassifiers for the perfect classification of epilepsy risk levels from EEG signals.Keywords: FMI, ICA, LGE, LDA, W-KNN, EEG
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Bai, Junjie, Kan Luo, Jun Peng, Jinliang Shi, Ying Wu, Lixiao Feng, Jianqing Li, and Yingxu Wang. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 4 (October 2017): 80–92. http://dx.doi.org/10.4018/ijcini.2017100105.
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Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.
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Singh, Law Kumar, Pooja, Hitendra Garg, and Munish Khanna. "Histogram of Oriented Gradients (HOG)-Based Artificial Neural Network (ANN) Classifier for Glaucoma Detection." International Journal of Swarm Intelligence Research 13, no. 1 (January 1, 2022): 1–32. http://dx.doi.org/10.4018/ijsir.309940.
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Glaucoma is a severe condition of the optic nerve resulting in the loss of eyesight. The proposed methodology has introduced the extraction of HOG (histogram of oriented gradients) features from the retinal fundus image. After the removal of HOG features, the authors compare the performance of five different machine learning techniques like k-nearest neighbour (KNN), support vector machine (SVM), linear discriminant analysis (LDA), naïve bayes, and artificial neural network. The process of image classification is based on analyzing the numerical properties of the obtained image features and classifying the data into different categories. In the paper, the authors intend to classify whether the image belongs to the glaucomatous category or the healthy category. After the application of the different classification algorithms to the test data and further analysis of the results, they could conclude that the SVM classifier provided an accuracy of 90%, KNN 86%, Naïve Bayes 96%, LDA 86%, and ANN 96.90% on the dataset in hand.
Dissertations / Theses on the topic "LDA+KNN CLASSIFIER"
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Pavani, Sri-Kaushik. "Methods for face detection and adaptive face recognition." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7567.
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The focus of this thesis is on facial biometrics; specifically in the problems of face detection and face recognition. Despite intensive research over the last 20 years, the technology is not foolproof, which is why we do not see use of face recognition systems in critical sectors such as banking. In this thesis, we focus on three sub-problems in these two areas of research. Firstly, we propose methods to improve the speed-accuracy trade-off of the state-of-the-art face detector. Secondly, we consider a problem that is often ignored in the literature: to decrease the training time of the detectors. We propose two techniques to this end. Thirdly, we present a detailed large-scale study on self-updating face recognition systems in an attempt to answer if continuously changing facial appearance can be learnt automatically.L'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.
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MANOJ, DIVI SAI. "COGNITIVE ASSESSMENT THROUGH THE ANALYSIS OF EEG SIGNALS." Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16577.
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With the advent of emerging technologies and methodologies of biomedical signal processing, research on the cognitive sciences has become one of the most innovative, enthralling and challenging researches in the field of biomedical engineering.
The problem of cognitive assessment and enhancement has gained major importance amongst the today’s cognitive researches, as it aids in identification and treatment of cognitive related disorders like Attention Deficit Hyperactivity Disorder (ADHD), Spatial Navigation, Short Term Memory, Cross Modal Processing etc.
In this work, as a part of Cognitive Assessment, we are concentrated towards the testing of Working Memory and Cognitive Workload through the analysis of the two channel ( - ) EEG signals obtained from the subjects when they were presented with some familiar stimulus. For this, the subjects were explained about the stimuli related to a situation and later were shown these stimuli and a model had been developed to differentiate these different types of stimuli responses. Several features in conjunction with classifiers have been explored and the corresponding results were analysed to decide the optimum feature and classifier that can be selected for obtaining the optimum classification accuracy.
A maximum classification accuracy of 66.67% had been obtained when tried with the LDA+RBFFNN classifier for -channel when the feature of Hurst Exponent on 3-5 IMFs is used for the task of inter Stimuli Classification and a maximum classification accuracy of 100% had been obtained when tried with the LDA+KNN classifier for and channels when the feature of Hurst Exponent is used for the task of inter Subject Classification and the results show a very good overall Inter subject classification accuracies for the classifier LDA+KNN.
Book chapters on the topic "LDA+KNN CLASSIFIER"
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Bai, Junjie, Kan Luo, Jun Peng, Jinliang Shi, Ying Wu, Lixiao Feng, Jianqing Li, and Yingxu Wang. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." In Cognitive Analytics, 1028–41. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch052.
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Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.
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D, Dr Kalaivani. "An Intrusion Detection System Based on Data Analytics and Convolutional Neural Network in NSS-KDD dataset." In Machine Learning Algorithms for Intelligent Data Analytics. Technoarete Research And Development Association, 2022. http://dx.doi.org/10.36647/mlaida/2022.12.b1.ch007.
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Due to the internet's quick growth, intrusion attacks have been growing exponentially, making them a very important worry in the modern era. Cyber-attacks can target any of the millions of users of the internet, as well as international companies and government agencies. The creation of sophisticated algorithms to identify these network breaches is therefore one of the most important tasks in the field of cyber-security research. In order to recognise malicious traffic inputs, intrusion detection systems (IDS) are trained using data from internet traffic logs. Utilizing these techniques, malicious traffic inputs are detected. The most often used database for internet traffic record data is that maintained by the Network Security Laboratory's Knowledge Discovery and Data Mining (NSL-KDD) team. It also acts as the benchmark for present-day internet traffic. This framework seeks to discriminate between normal and abnormal (Denial of Service (DoS), Probe, User to Root (U2R), and Remote to Local (R2L)) categories in the NSL-KDD database with high detection precision and low false alarm rates. Several classifiers, including Naive Bayes (NB), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), linear discriminant analysis (LDA), and Convolution Neural Network, will be used to achieve this (CNN). The unique and cutting-edge supervised detection techniques will be used in this study as the fundamental approaches to address the issue of the need for more labelled data during the IDS training process. The results of the trials show that, in terms of classification performance, the CNN classifier outperforms both recently presented approaches and other methods that are currently in use.
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Ghanem, Khadoudja. "Local and Global Latent Semantic Analysis for Text Categorization." In Information Retrieval and Management, 1360–74. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch060.
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In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which is 20 Newsgroup corpus. Obtained results show the effectiveness of the method compared with those obtained with the classic KNN and SVM classifiers as well as with methods presented in the literature. Experimental results show that the new method has high precision and recall rates and classification accuracy is significantly improved.
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S, Sheela, Sumathi M, Sumathy S, Thirumoorthy S, and Subalakshmi E. "Analysis of Various Textural Descriptors for Ovarian Cyst Classification." In Intelligent Systems and Computer Technology. IOS Press, 2020. http://dx.doi.org/10.3233/apc200208.
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Ovarian cyst is one of the main causes of infertility. Ovarian cancers are also caused by the ovarian cyst that grows in the ovary. An ovarian cyst can be benign (non-cancerous) or malignant (cancerous). If the cyst is not diagnosed and treated at the earliest, the benign cyst may turn into malignant and can be fatal. Various image processing techniques are used to assist the clinicians to characterize the ovarian cyst using the textural descriptor. This paper reviews several textural descriptors for feature extraction like Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Optimal Oriented Pattern (LOOP). Finally, extracted features are applied to SVM, KNN and Ensemble classifiers to compare the performance of the textural descriptors.
Conference papers on the topic "LDA+KNN CLASSIFIER"
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Mittal, Harshit. "Evaluating The Performance of Feature Extraction Techniques Using Classification Techniques." In 4th International Conference on NLP Trends & Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131402.
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Dimensionality reduction techniques are widely used in machine learning to reduce the computational complexity of the model and improve its performance by identifying the most relevant features. In this research paper, we compare various dimensionality reduction techniques, including Principal Component Analysis(PCA), Independent Component Analysis(ICA), Local Linear Embedding(LLE), Local Binary Patterns(LBP), and Simple Autoencoder, on the Olivetti dataset, which is a popular benchmark dataset in the field of face recognition. We evaluate the performance of these dimensionality reduction techniques using various classification algorithms, including Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The goal of this research is to determine which combination of dimensionality reduction technique and classification algorithm is the most effective for the Olivetti dataset. Our research provides insights into the performance of various dimensionality reduction techniques and classification algorithms on the Olivetti dataset. These results can be useful in improving the performance of face recognition systems and other applications that deal with high-dimensional data.
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Cardoso, Isadora, Heitor Ramos, and Eliana Almeida. "Classificação de Doenças Intersticiais Pulmonares Difusas através de Tomografia Computadorizada de Alta-Resolução." In XVI Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbcas.2016.9908.
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O objetivo deste trabalho é auxiliar no desenvolvimento de uma ferramenta de diagnóstico de doenças pulmonares auxiliado por computador. Nessa primeira etapa utilizamos análise de componentes principais (PCA), análise do discriminante linear (LDA) e o algoritmo de k-vizinhos mais próximos (KNN) para classificar 3252 regiões de interesse (ROI) de Tomografias Computadorizadas de Alta-Resolução de tórax em relação à 6 padrões pulmonares. Cada ROI possui um total de 28 dimensões que foram reduzidas por PCA e LDA e então classificadas por KNN (k = 5). Obtivemos uma taxa de classificação correta de 80,82% em 13 dimensões com PCA e 83,74% em 5 dimensões com LDA.