Wyniki wyszukiwania - Biblioteka Nauki

1

Feature selection for breast cancer malignancy classification problem

100%

Filipczuk P. , Kowal M. , Marciniak A.

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tom Vol. 15

193--199

EN

The paper provides a preview of some work in progress on the computer system to support breast cancer diagnosis. Diagnosis approach is based on microscope images of the FNB (Fine Needle Biopsy) and assumes distinguishing malignant from benign cases. Studies conducted focus on two different problems, the first concern the extraction of morphometric parameters of nuclei present in cytological images and the other concentrate on breast cancer nature classification using selected features. Studies in both areas are conducted in parallel. This work is devoted to the problem of feature selection from the set of determined features in order to maximize the accuracy of classification. Morphometric features are derived directly from a digital scans of breast fine needle biopsy slides and are computed for segmented nuclei. The quality of feature space is measured with four different classification methods. In order to illustrate the effectiveness of the approach, the automatic system of malignancy classification was applied on a set of medical images with promising results.

2

Feature selection using particle swarm optimization in text categorization

84%

Aghdam M. H. , Heidari S.

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tom Vol. 5, No. 4

231--238

EN

Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.

3

A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm

84%

Kavi Priya S. , Pon Karthika K.

International Journal of Applied Mathematics and Computer Science

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2023

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tom Vol. 33, no. 1

117--131

EN

Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.

4

An improved facial image retrieval using hybrid cheetah optimization algorithm

84%

Balasubramanian C. , Raja Sekar J.

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tom Vol. 72, nr 2

art. no. e148942

EN

With the advent of social media, the volume of photographs uploaded on the internet has increased exponentially. The task of efficiently recognizing and retrieving human facial images is inevitable and essential at this time. In this work, a feature selection approach for recognizing and retrieving human face images using hybrid cheetah optimization algorithm is proposed. The deep feature extraction from the images is done using deep convolutional neural networks. Hybrid cheetah optimization algorithm, an improvised version of cheetah optimization algorithm fused with genetic algorithm is used, to choose optimum features from the extracted deep features. The chosen features are used for finding the best-matching images from the image database. The image matching is performed by approximate nearest neighbor search for the query image over the image database and similar images are retrieved. By constructing a k-NN graph for the images, the efficiency of image retrieval is enhanced. The proposed system performance is evaluated against benchmark datasets such as LFW, MultiePie, ColorFERET, DigiFace-1M and CelebA. The evaluation results show that the proposed methodology is superior to various existing methodologies.

5

An example of computer aided decision-making system for recognition of respiration pathology

67%

Jóźwik A. , Sokołowska B. , Budzińska K.

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tom Vol. 2, Part. 1

MI41--MI48

EN

A main objective of the work was presentation of a new statistic approach to an analysis of respiration data. The breathing with intact and denervated diaphragm was compared. The respiration process was desciribed by three parameters: breathing frequency, tidal volume, and minute ventilation. Experimental data concerned a group of twelve anaesthetised cats. These data were analysed by a modification of the well-known k nearest neighbour rule (k-NN). It has been adopted from the statistical pattern recognition theory. The three ventilatory parameters were used to recognise whether we deal with the normal or the pathological case. Certain percentage of misclassifications must be taken into account. This misclassification rate is a measure how strong is the dependence between the ventilation parameters and preservation of the diaphragm innervation. The proposed method promises good differentiation of the two compared ways of respiration. It offers nearly five times smaller misclassification rate as compared with the standard k-NN rule.

6

Some problems with construction of the k-NN classifier for recognition of an experimental respiration pathology

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Jóźwik A. , Sokołowska B.

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tom Vol. 3

MI89--97

EN

An objective of the work is to demonstrate some difficulties with construction of a classifier based on the k-NN rule. The standard k-NN classifier and the parallel k-NN classifier have been chosen as the two most powerful approaches. This kind of classifiers has been applied to automatic recognition of diaphragm paralysis degree. The classifier construction consists in determination of the number of nearest neighbors, selection of features and estimation of the classification quality. Three classes of muscle pathology, including the control class, and five ventilatory parameters are taken into account. The data concern a model of the diaphragm pathology in a cat. The animals were forced to breathe in three different experimental situations: air, hypercapnic and hypoxic conditions. A separate classifier is constructed for each kind of the mentioned situations. The calculation of the misclassification rate is based on the leave one out and on the testing set method. Several computational experiments are suggested for the correct feature selection, the classifier type choice and the misclassification probability estimation.

7

Application of k-NN classifier and Fisher-test in analysis of cells culture model

67%

Jóźwik A. , Faff L. , Sokołowska B.

Journal of Medical Informatics & Technologies

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2000

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tom Vol. 1

CS 15--20

EN

The paper deals with determination of the LPS factor influence and the significance of Na+ -contained and Na+ -free HEPES solution on a behavior of microglial cells cultured in vitro. A behavior of microglial cells is characterized by 14 parameters. The dependence between these parameters and a presence of LPS factor or natrium ions has been studied by use of the “k nearest neighbor” (k-NN) rule taken from the pattern recognition theory. The obtained computational results were verified by the Fisher test.