1,182 research outputs found

    Elephant Search with Deep Learning for Microarray Data Analysis

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    Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

    Computational models and approaches for lung cancer diagnosis

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    The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

    An Oversampling Mechanism for Multimajority Datasets using SMOTE and Darwinian Particle Swarm Optimisation

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    Data skewness continues to be one of the leading factors which adversely impacts the machine learning algorithms performance. An approach to reduce this negative effect of the data variance is to pre-process the former dataset with data level resampling strategies. Resampling strategies have been seen in two forms, oversampling and undersampling. An oversampling strategy is proposed in this article for tackling multiclass imbalanced datasets. This proposed approach optimises the state-of-the-art oversampling technique SMOTE with the Darwinian Particle Swarm Optimization technique. This proposed method DOSMOTE generates synthetic optimised samples for balancing the datasets. This strategy will be more effective on multimajority datasets.  An experimental study is performed on peculiar multimajority datasets to measure the effectiveness of the proposed approach. As a result, the proposed method produces promising results when compared to the conventional oversampling strategies

    Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy

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    Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind power generator.НаиболСС распространСнныС ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ построСния ΠΌΡƒΠ»ΡŒΡ‚ΠΈΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ классификации Π·Π°ΠΊΠ»ΡŽΡ‡Π°ΡŽΡ‚ΡΡ Π² ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°Π±ΠΎΡ€Π° Π΄Π²ΠΎΠΈΡ‡Π½Ρ‹Ρ… классификаторов ΠΈ ΠΈΡ… объСдинСнии. Π’ Π΄Π°Π½Π½ΠΎΠΉ ΡΡ‚Π°Ρ‚ΡŒΠ΅ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° машина ΠΎΠΏΠΎΡ€Π½Ρ‹Ρ… Π²Π΅ΠΊΡ‚ΠΎΡ€ΠΎΠ² с классификатором Π²Ρ‹Ρ…ΠΎΠ΄Π½Ρ‹Ρ… ΠΊΠΎΠ΄ΠΎΠ² исправлСния ошибок(ECOC-SVM) с Ρ†Π΅Π»ΡŒΡŽ ΠΊΠ»Π°ΡΡΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ ΠΈ Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΠΎΠ²Π°Ρ‚ΡŒ Ρ‚Π°ΠΊΠΈΠ΅ Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΡ качСства элСктроэнСргии, ΠΊΠ°ΠΊ гармоничСскиС искаТСния, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ напряТСния ΠΈ скачок напряТСния, Π²ΠΊΠ»ΡŽΡ‡Π°Ρ Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ Π²Π΅Ρ‚Ρ€ΠΎΠ²Ρ‹Ρ… элСктростанций Π² систСмах ΠΏΠ΅Ρ€Π΅Π΄Π°Ρ‡ΠΈ элСктроэнСргии. Π‘Π½Π°Ρ‡Π°Π»Π° выполняСтся Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡ‚ΠΎΠΊΠ° нСсиммСтричной Π½Π°Π³Ρ€ΡƒΠ·ΠΊΠΈ Ρ‚Ρ€Π΅Ρ… Ρ„Π°Π· для расчСта разностных характСристик элСктричСской сСти, ΡƒΡ€ΠΎΠ²Π½Π΅ΠΉ напряТСния, Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ ΠΈ Ρ€Π΅Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ мощности. ПослС этого дискрСтноС Π²Π΅ΠΉΠ²Π»Π΅Ρ‚-ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡŠΠ΅Π΄ΠΈΠ½ΡΠ΅Ρ‚ΡΡ с вСроятностной модСлью ECOC-SVM для построСния классификатора. НаконСц, ECOC-SVM классифицируСт ΠΈ ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΡ†ΠΈΡ€ΡƒΠ΅Ρ‚ Ρ‚ΠΈΠΏ возмущСния Π² соотвСтствии с ΠΎΡ‚ΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ΠΌ энСргии дискрСтного Π²Π΅ΠΉΠ²Π»Π΅Ρ‚-прСобразования. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½Ρ‹ΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ Π΄Π°Π΅Ρ‚ ΡƒΠ΄ΠΎΠ²Π»Π΅Ρ‚Π²ΠΎΡ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΡƒΡŽ Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ 99,2% ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с Ρ…ΠΎΡ€ΠΎΡˆΠΎ извСстными ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ ΠΈ ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚, Ρ‡Ρ‚ΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠ΅ Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΠ΅ качСства элСктроэнСргии ΠΈΠΌΠ΅Π΅Ρ‚ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½Ρ‹Π΅ отклонСния ΠΎΡ‚ чисто ΡΠΈΠ½ΡƒΡΠΎΠΈΠ΄Π°Π»ΡŒΠ½ΠΎΠΉ Ρ„ΠΎΡ€ΠΌΡ‹ Π²ΠΎΠ»Π½Ρ‹, Ρ‡Ρ‚ΠΎ способствуСт Ρ€Π°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡŽ ΠΈ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΡŽ Ρ‚ΠΈΠΏΠ° возмущСния, Π³Π΅Π½Π΅Ρ€ΠΈΡ€ΡƒΠ΅ΠΌΠΎΠ³ΠΎ Π²Π΅Ρ‚Ρ€ΠΎΠ²Ρ‹ΠΌ Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ΠΎΠΌ

    Effective Detection of Parkinson’s Disease at Different Stages using Measurements of Dysphonia

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    This paper addressees the problem of multiclass of Parkinson’s disease by the characteristic features of person’s voice. So we computed 22 dysphonia measures from 375 voice samples of healthy and people suffer from PD. We used the particle swarm optimization (PSO) feature selection method, with random forest and the linear discriminant analysis (LDA) along with the 4-fold cross validation analysis to classify the subjects in 4 classes according to the severity of symptoms. With a classification accuracy score of 95.2%. Promisingly, the proposed diagnosis system might serve as a powerful tool for diagnosing PD, and could also extended for other voice pathologies

    Komparasi Dan Analisis Kinerja Model Algoritma SVM Dan PSO-SVM (Studi Kasus Klasifikasi Jalur Minat SMA)

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    Attribute Selection is very important for classification process. This research has been done by doing attribute selection using PSO method (Particle Swarm Optimization) on SVM algorithm (Support Vector Machine). The development of the classification model uses three parameters especially data attribute, influence of the transformation of various kernel function and penalty factor (C) toward the performance of SVM and PSO-SVM classification. The analysis uses five kernels in mySVM library that existed in Rapidminer application namely dot, radial, polynomial, neural, and anova kernel. The training data used in the first model classification development is student interest data at ABC high school on 2013-2014 year academic. The first model is evaluated using accuracy, precision, recall, and auc value test. The first result shows that the anova kernel on PSO-SVM is able to work with accuracy level 99.30% using penalty factor 0.1. The second model has been developed to predict student interest in XYZ high school. The second result shows that PSO-SVM with kernel anova is able to classify students interest with 99.29% accuracy level. Keywordsβ€” Optimization, SVM, PSO-SVM, Student Interest

    Hybrid ACO and SVM algorithm for pattern classification

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    Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO
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