A new genetic algorithm for multi-label correlation-based feature selection.

This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subset, in order to select a high-quality feature subset is used by a multi-label classification algorithm - in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the previously proposed Hill-Climbing for Multi-Label Correlation-Based Feature Selection (HC-ML-CFS), across 10 multi-label datasets

Distributed Genetic Algorithm for Feature Selection

We empirically show that process-based Parallelism speeds up the Genetic Algorithm (GA) for Feature Selection (FS) 2x to 25x, while additionally increasing the Machine Learning (ML) model performance on metrics such as F1-score, Accuracy, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC)

انتخاب هزینه های تمایزگذار بین دو روش درمانی آنژیوپلاستی اولیه و ترومبولیتیک درمانی با رویکرد پیوند الگوریتم ژنتیک و درخت تصمیم

زمینه و هدف: انفارکتوس قلبی شایعترین علت مرگ‌و‌میر در کشورهای توسعه‌یافته و در‌حال‌توسعه می‌باشد که طبق پیش‌بینی سازمان بهداشت جهانی تا سال 2020 میلادی علت اصلی مرگ در کل دنیا خواهد بود. هدف اصلی از درمان انفارکتوس حاد برقراری مجدد جریان خون است که در درمان آن‌ دو رویکرد درمانی وجود دارد که این دو رویکرد گرچه برای درمان یک عارضه به کار بسته می‌شوند ولی از نظر کیفیت درمان و هزینه باهم متفاوت هستند؛ از آن‌جا که هزینه‌های بهداشتی درمانی در حال افزایش بوده و بررسی آن‌ها از اهمیت زیادی برخوردار است، هدف این تحقیق یافتن مشخصات هزینه‌ای تاثیرگذار در درمان بیماران سکته حاد قلبی است، تا چارچوبی برای مقایسه دو روش درمانی ارائه گردد. مواد و روش ها: مدل ارائه‌شده در تحقیق حاضر، برای انتخاب مشخصات هزینه‌ای تمایزگذار از پیوند الگوریتم ژنتیک و درخت تصمیم به عنوان رویکردی جدید استفاده کرده‌ و با مقایسه نتایج آن با روش جنگل تصادفی اعتبارسنجی صورت گرفته است. نتایج: نتایج نشان‌دهنده کاهش خطای دسته‌بندی بوده و به کاهش خطای تقریبی 0.2 نسبت به روش جنگل تصادفی رسیدیم. سپس با توجه به مشخصه‌های انتخابی، چارچوبی برای مقایسه دو روش درمانی ارائه گردیده‌است. نهایتا روش آنژیوپلاستی اولیه در اکثر هزینه‌های مستقیم هزینه کمتری داشته اما در هزینه‌های غیر‌مستقیم در برخی موارد به نتایجی خلاف آنچه مورد انتظار پزشکان بود رسیدیم. نتیجه گیری: رویکرد پیشنهادی در این تحقیق می‌تواند راهنمایی برای پزشکان و موسسات بیمه در سیاست گذاری‌های درمان انفارکتوس حاد باشد

Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI

<p>Abstract</p> <p>Background</p> <p>Near-infrared spectroscopy (NIRS) is an optical imaging technology that has recently been investigated for use in a safe, non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. To date, most NIRS-BCI studies have attempted to discriminate two mental states (e.g., a mental task and rest), which could potentially lead to a two-choice BCI system. In this study, we attempted to automatically differentiate three mental states - specifically, intentional activity due to 1) a mental arithmetic (MA) task and 2) a mental singing (MS) task, and 3) an unconstrained, "no-control (NC)" state - to investigate the feasibility of a three-choice system-paced NIRS-BCI.</p> <p>Results</p> <p>Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations while 7 able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a ten-dimensional feature set, an overall classification accuracy of 56.2% was achieved for the MA vs. MS vs. NC classification problem and all individual participant accuracies significantly exceeded chance (i.e., 33%). However, as anticipated based on results of previous work, the three-class discrimination was unsuccessful for three participants due to the ineffectiveness of the mental singing task. Excluding these three participants increases the accuracy rate to 62.5%. Even without training, three of the remaining four participants achieved accuracies approaching 70%, the value often cited as being necessary for effective BCI communication.</p> <p>Conclusions</p> <p>These results are encouraging and demonstrate the potential of a three-state system-paced NIRS-BCI with two intentional control states corresponding to mental arithmetic and mental singing.</p

Generic Feature Selection and Document Processing

International audienceThis paper presents a generic features selection method and its applications on some document analysis problems. The method is based on a genetic algorithm (GA), whose fitness function is defined by combining Adaboot classifiers associated with each feature. Our method is not linked to a classifier achieving the final recognition task; we have used a combination of weak classifiers to evaluate a subset of features. So we select features that can further be used in the most appropriate classifiers. This method has been tested on three applications: Drop caps classification, handwritten digits recognition and text detection. The results show the efficiency and robustness of the proposed approach

Enhancing feature selection with a novel hybrid approach incorporating genetic algorithms and swarm intelligence techniques

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance

A Review on Feature Selection Methods For Classification Tasks

Abstract: In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal method that achieves the best classification accuracy with fewer features is still an open research area

Sélection de caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost

International audienceCet article se situe dans la problématique de la sélection de caractéristiques. Nous proposons une méthode rapide basée sur un algorithme génétique et utilisant la combinaison de classifieurs Adaboost. L'évaluation des individus dans l'algorithme génétique se fait par une fonction de "fitness" basée sur la combinaison de classifieurs entraînés par Adaboost pour chacune des caractéristiques. Cette méthode est implémentée et testée sur la base des images de chiffres manuscrits MNIST et les résultats montrent la robustesse de notre approche ainsi que ses performances. En moyenne le nombre de caractéristiques est divisé par deux sans pour autant diminuer les taux de reconnaissance des images