MaZda—A software package for image texture analysis Szczypiński, Piotr M; Strzelecki, Michał; Materka, Andrzej ...
Computer methods and programs in biomedicine,
04/2009, Letnik:
94, Številka:
1
Journal Article
Recenzirano
Abstract MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture ...features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage.
Classification and recognition of graph data are crucial problems in many fields, such as bioinformatics, chemoinformatics and data mining. In graph kernel-based classification methods, the ...similarity among substructures is not fully considered; in addition, poorly discriminative substructures will affect the graph classification accuracy. To improve the graph classification accuracy, we propose a feature reduction algorithm based on semantic similarity for graph classification in this paper. In the algorithm, we first learn vector representations of subtree patterns using neural language models and then merge semantically similar subtree patterns into a new feature. We then provide a new feature discrimination score to select highly discriminative features. Comprehensive experiments on real datasets demonstrate that the proposed algorithm achieves a significant improvement in classification accuracy over compared graph classification methods.
Defect prediction is an important task for preserving software quality. Most prior work on defect prediction uses software features, such as the number of lines of code, to predict whether a file or ...commit will be defective in the future. There are several reasons to keep the number of features that are used in a defect prediction model small. For example, using a small number of features avoids the problem of multicollinearity and the so-called ‘curse of dimensionality’. Feature selection and reduction techniques can help to reduce the number of features in a model. Feature selection techniques reduce the number of features in a model by selecting the most important ones, while feature reduction techniques reduce the number of features by creating new, combined features from the original features. Several recent studies have investigated the impact of feature
selection
techniques on defect prediction. However, there do not exist large-scale studies in which the impact of multiple feature
reduction
techniques on defect prediction is investigated. In this paper, we study the impact of eight feature reduction techniques on the performance and the variance in performance of five supervised learning and five unsupervised defect prediction models. In addition, we compare the impact of the studied feature reduction techniques with the impact of the two best-performing feature selection techniques (according to prior work). The following findings are the highlights of our study: (1) The studied correlation and consistency-based feature selection techniques result in the best-performing supervised defect prediction models, while feature reduction techniques using neural network-based techniques (restricted Boltzmann machine and autoencoder) result in the best-performing unsupervised defect prediction models. In both cases, the defect prediction models that use the selected/generated features perform better than those that use the original features (in terms of AUC and performance variance). (2) Neural network-based feature reduction techniques generate features that have a small variance across both supervised and unsupervised defect prediction models. Hence, we recommend that practitioners who do not wish to choose a best-performing defect prediction model for their data use a neural network-based feature reduction technique.
Multi-label learning for large-scale data is a grand challenge because of a large number of labels with a complex data structure. Hence, the existing large-scale multi-label methods either have ...unsatisfactory classification performance or are extremely time-consuming for training utilizing a massive amount of data. A broad learning system (BLS), a flat network with the advantages of succinct structures, is appropriate for addressing large-scale tasks. However, existing BLS models are not directly applicable for large-scale multi-label learning due to the large and complex label space. In this work, a novel multi-label classifier based on BLS (called BLS-MLL) is proposed with two new mechanisms: kernel-based feature reduction module and correlation-based label thresholding. The kernel-based feature reduction module contains three layers, namely, the feature mapping layer, enhancement nodes layer, and feature reduction layer. The feature mapping layer employs elastic network regularization to solve the randomness of features in order to improve performance. In the enhancement nodes layer, the kernel method is applied for high-dimensional nonlinear conversion to achieve high efficiency. The newly constructed feature reduction layer is used to further significantly improve both the training efficiency and accuracy when facing high-dimensionality with abundant or noisy information embedded in large-scale data. The correlation-based label thresholding enables BLS-MLL to generate a label-thresholding function for effective conversion of the final decision values to logical outputs, thus, improving the classification performance. Finally, experimental comparisons among six state-of-the-art multi-label classifiers on ten datasets demonstrate the effectiveness of the proposed BLS-MLL. The results of the classification performance show that BLS-MLL outperforms the compared algorithms in 86% of cases with better training efficiency in 90% of cases.
The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more ...precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate.
This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation.
The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition.
From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers.
In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.
•Cognitive driven gait detection and classification using computational techniques.•Gait pattern identification with help of Wearable acceleration sensors.•Dimensionality reduction improves gait data classification accuracy.•Random Forest classifier outperform other discriminants and achieves 98 % accuracy.
In this paper, a novel method for affect detection is presented. The method combines both connectivity-based and channel-based features with a selection method that considerably reduces the ...dimensionality of the data and allows for an efficient classification. In particular, the Relative Energy (RE) and its logarithm in the spacial domain, and the Spectral Power (SP) in the frequency domain are computed for the four typical frequency bands (α, β, γ and θ), and complemented with the Mutual Information measured over all channel pairs. The resulting features are then reduced by using a hybrid method that combines supervised and unsupervised feature selection. First, Welch’s t-test is used to select the features that best separate the classes, and discard the ones that are less useful for classification. To this end, all features where the t-test yields a p-value above a threshold are eliminated. The remaining ones are further reduced by using Principal Component Analysis. Detection results are compared to state-of-the-art methods on DEAP, a database for emotion analysis composed of labeled recordings from 32 subjects while watching 40 music videos. The effect of using different classifiers is also evaluated, and a significant improvement is observed in all cases.
This paper presents an effective electrocardiogram (ECG) arrhythmia classification scheme consisting of a feature reduction method combining principal component analysis (PCA) with linear ...discriminant analysis (LDA), and a probabilistic neural network (PNN) classifier to discriminate eight different types of arrhythmia from ECG beats. Each ECG beat sample composed of 200 sampling points at a 360Hz sampling rate around an R peak is extracted from ECG signals. The feature reduction method is employed to find important features from ECG beats, and to improve the classification accuracy of the classifier. With the selected features, the PNN is then trained to serve as a classifier for discriminating eight different types of ECG beats. The average classification accuracy of the proposed scheme is 99.71%. Our experimental results have successfully validated that the integration of the PNN classifier with the proposed feature reduction method can achieve satisfactory classification accuracy.
Lung sounds provide essential information about the health of the lungs and
respiratory tract. They have unique and distinguishable patterns associated
with the abnormalities in these organs. Many ...studies attempted to develop
various methods to classify lung sounds automatically. Wavelet transform is
one of the approaches widely utilized for physiological signal analysis.
Commonly, wavelet in feature extraction is used to break down the lung
sounds into several sub-bands before calculating some parameters. This study
used five lung sound classes obtained from various sources. Furthermore, the
wavelet analysis process was carried out using Discrete Wavelet Transform
(DWT) and Wavelet Package Decomposition (WPD) analysis and entropy
calculation as feature extraction. In the DWT process, the highest accuracy
obtained was 97.98% using Permutation Entropy (PE), Renyi Entropy (RE), and
Spectral Entropy (SEN). In WPD, the best accuracy achieved is 98.99 % when 8
sub-bands and RE are used. These results are relatively competitive compared
with previous studies using the wavelet method with the same datasets.
To improve the effectiveness of surrogate-assisted evolutionary algorithms (SAEAs) in solving high-dimensional expensive optimization problems with multi-polar and multi-variable coupling properties, ...a new approach called DRBM-ASRL is proposed. This approach leverages restricted Boltzmann machines (RBMs) for feature learning and reinforcement learning for adaptive strategy selection. DRBM-ASRL integrates four search strategies based on three heterogeneous surrogate modeling approaches, each catering to different preferences. Two of these strategies focus on generative sampling in the subspaces with varying dimensions, while the other two aim to explore the local and global landscapes in the high-dimensional source space. This allows for more effective tradeoffs between exploration and exploitation in the solution space. Reinforcement learning is employed to adaptively prioritize the search strategies during optimization , based on the online feedback information from the optimal solution. In addition, to enhance the representation of potentially optimal samples in the solution space, two task-driven RBMs are separately trained to construct a feature subspace and reconstruct the features of the source space. DRBM-ASRL has been evaluated on various high-dimensional benchmarks ranging from 50 to 200 dimensions, as well as 14 CEC 2013 complex benchmark problems with 100 dimensions and a power system problem with 118 dimensions. Experimental results demonstrate its superior convergence performance and optimization efficiency compared to eight state-of-the-art SAEAs.
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