A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking the features with respect to their relevance. This paper proposes an ensemble approach for Feature Ranking, aggregating feature rankings extracted along independent runs of a...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

Recent research has proved the beneets of using an ensemble of diverse and accurate base classiiers for classiication problems. In this paper the focus is on producing diverse ensembles with the aid of three feature selection heuristics based on two approaches: correlation and contextual merit-based ones. We have developed an algorithm and experimented with it to evaluate and compare the three ...

Maximum Echo-State-Likelihood Networks for Emotion Recognition Edmondo Trentin, Stefan Scherer, aand Friedhelm Schwenker Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysis Tsuneyoshi Ishii and Shigeo Abe Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiers Rakkrit Duangsoithong and Terry Windeatt A New Monte Carlo-based Error Rate Estimator Ah...

objective(s): this study addresses feature selection for breast cancer diagnosis. the present process uses a wrapper approach using ga-based on feature selection and ps-classifier. the results of experiment show that the proposed model is comparable to the other models on wisconsin breast cancer datasets. materials and methods: to evaluate effectiveness of proposed feature selection method, we ...

In this paper, we propose a novel learning method for face detection using discriminative feature selection. The main deficiency of the boosting algorithm for face detection is its long training time. Through statistical learning theory, our discriminative feature selection method can make the training process for face detection much faster than the boosting algorithm without degrading the gene...

The shift in paradigm with advanced Machine Learning algorithms will help to face the challenges such as computational power, training time, and algorithmic stability. individual feature selection techniques, hardly give appropriate subsets, that might be vulnerable variations induced at input data thus led wrong conclusions. An expedient technique should designed for approximating relevance im...

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals diagnosis neurological pathologies, current challenge is improve reliability tools classify or detect abnormalities. In this study, we used an ensemble feature selection approach integrate advantages several algorithms identification characteristic...