FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS

Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairmen

A New Approach of Rough Set Theory for ‎Feature Selection and Bayes Net Classifier ‎Applied on Heart Disease Dataset

درسنا في هذا البحث اختيار الصفات بالاعتماد على نهج جديد من  خوارزمية مجموعة التقريب حيث تعتمد هذه الطريقة على اختيار الصفات الأكثر تاثيرا. لجئنا الى انتقاء الصفات اختصارا للوقت , وجود الصفة تؤثر على دقة النتائج او قد تكون الصفة غير متوفرة . تم تطبيق الخوارزمية على بيانات امراض القلب لاختيار افضل الصفات المؤثرة. ان المشكلة الرئيسية هو كيفية تشخيص الإصابة فيما لو كان مصاب بمرض القلب من عدمه.هذه المشكلة تمثل تحدي لان لا نسطيع اتخاذ القرار بصورة مباشرة. تعتمد الطريقة المقترحة على ترميز البيانات الاصلية .ان الناتج من هذه الخوارزميه هي الصفات الأكثر أهمية حيث تهمل الصفات السيئة والغير ضرورية.وتم تطبيق النتائج على خوارزمية شكبة بيزينت كخوارزمية للتنبؤ بالمرض وقد حصلنا على النتائج 82.17 , 83.49 , 74.58 عند استخدام جميع الصفات ,12 , 7 طول الصفات على التوالي.وتم تطبيق نتائج خوارزمية مجموعة التقريب الاصلية على خوارزمية البيزين وحصلنا على النتائج 58.41 ,81.51  عند استخدام 2 , 12 طول الصفات على التواليIn this paper a new approach of rough set features selection has been proposed. Feature selection has been used for several reasons a) decrease time of prediction b) feature possibly is not found c) present of feature case bad prediction. Rough set has been used to select most significant features. The proposed rough set has been applied on heart diseases data sets. The main problem is how to predict patient has heart disease or not depend on given features. The problem is challenge, because it cannot determine decision directly .Rough set has been modified to get attributes for prediction by ignored unnecessary and bad features. Bayes net has been used for classified method. 10-fold cross validation is used for evaluation. The Correct Classified Instances were 82.17, 83.49, and 74.58 when use full, 12, 7 length of attributes respectively. Traditional rough set has been applied, the minimum Correct Classified Instances were 58.41 and 81.51 when use 2 length of attributes respectivel

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

ANALYSIS OF SERVICE SATISFACTION LEVEL USING ROUGH SET ALGORITHM

Data mining Is a technique that combines traditional data analysis techniques with algorithms for processing large amounts of data. Data mining can be used to perform data analysis and find important patterns in data. Data mining will be a benchmark or reference for making data mining processing decisions that can be done with the Rough Set method. Rough Set Method is one of the methods above that allows us to make decisions in hotel services because in this method there are formulations or stages of problem mechanics and a Result (decision) of a combination that may occur from the criteria above. From the results (decisions) derived from the processed data mining, it can be used as a reference for decision making. The Rought Set Method is a mathematical technique developed since 1980

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

Software quality is an important research area and has gain considerable attention from software engineering community in identification of priority quality attributes in software development process. This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. The proposed algorithm is able to improve the weaknesses in PQF model in updating and learning the important attributes for software quality assessment. The existing assessment techniques lack of the capability to rank the quality attributes and data learning which can enhance the quality assessment process. The aim of the study is to identify and propose the application of Artificial Intelligence (AI) technique for improving quality assessment technique in PQF model. Therefore, FRA using FRT was constructed and the performance of the FRA was evaluated. The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. The result shows that the performance of FRA correlates strongly to PQF model with 98% correlation compared to the Kolmogorov-Smirnov Correlation Based Filter (KSCBF) algorithm with 83% correlation. Statistical significance test was also performed with score of 0.052 compared to the KSCBF algorithm with score of 0.048. The result shows that the FRA was more significant than KSCBF algorithm. The main contribution of this research is on the implementation of FRT with proposed Most Priority of Features (MPF) calculation in FRA for attributes assessment. Overall, the findings and contributions can be regarded as a novel effort in software quality for attributes selection

Multi-modal association learning using spike-timing dependent plasticity (STDP)

We propose an associative learning model that can integrate facial images with speech signals to target a subject in a reinforcement learning (RL) paradigm. Through this approach, the rules of learning will involve associating paired stimuli (stimulus–stimulus, i.e., face–speech), which is also known as predictor-choice pairs. Prior to a learning simulation, we extract the features of the biometrics used in the study. For facial features, we experiment by using two approaches: principal component analysis (PCA)-based Eigenfaces and singular value decomposition (SVD). For speech features, we use wavelet packet decomposition (WPD). The experiments show that the PCA-based Eigenfaces feature extraction approach produces better results than SVD. We implement the proposed learning model by using the Spike- Timing-Dependent Plasticity (STDP) algorithm, which depends on the time and rate of pre-post synaptic spikes. The key contribution of our study is the implementation of learning rules via STDP and firing rate in spatiotemporal neural networks based on the Izhikevich spiking model. In our learning, we implement learning for response group association by following the reward-modulated STDP in terms of RL, wherein the firing rate of the response groups determines the reward that will be given. We perform a number of experiments that use existing face samples from the Olivetti Research Laboratory (ORL) dataset, and speech samples from TIDigits. After several experiments and simulations are performed to recognize a subject, the results show that the proposed learning model can associate the predictor (face) with the choice (speech) at optimum performance rates of 77.26% and 82.66% for training and testing, respectively. We also perform learning by using real data, that is, an experiment is conducted on a sample of face–speech data, which have been collected in a manner similar to that of the initial data. The performance results are 79.11% and 77.33% for training and testing, respectively. Based on these results, the proposed learning model can produce high learning performance in terms of combining heterogeneous data (face–speech). This finding opens possibilities to expand RL in the field of biometric authenticatio