This paper investigates an ensemble feature selection algorithm that is based on genetic algorithms. The task of ensemble feature selection is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble’s classifiers...

In this paper, we present an ordinal feature based method for face recognition. Ordinal features are used to represent faces. Hamming distance of many local sub-windows is computed to evaluate differences of two ordinal faces. AdaBoost learning is finally applied to select most effective hamming distance based weak classifiers and build a powerful classifier. Experiments demonstrate good result...

In this paper, we evaluate a variety of knowledge sources and supervised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data. Our knowledge sources include the part-of-speech of neighboring words, single words in the surrounding context, local collocations, and syntactic relations. The learning algorithms evaluated include Support Vector Machines (SVM), Naive Bay...

We present an analysis of a general machine learning technique called ‘stacking’ for the estimation of photometric redshifts. Stacking techniques can feed the photometric redshift estimate, as output by a base algorithm, back into the same algorithm as an additional input feature in a subsequent learning round. We shown how all tested base algorithms benefit from at least one additional stackin...

Objectives: Given the importance of accurate prediction financial time series data and their benefits in real-life, AdaBoost-GRU ensemble learning is proposed which it’s forecasting accuracy to be compared with AdaBoost-LSTM, single Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU). Methods: The for Korea Composite Stock Price Index (KOSPI) obtained from Naver Finance January 2000 April...

With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...

An automatic Facial Expression Recognition (FER) model with Adaboost face detector, feature selection based on manifold learning and synergetic prototype based classifier has been proposed. Improved feature selection method and proposed classifier can achieve favorable effectiveness to performance FER in reasonable processing time.

This paper presents a gesture-based Human-Computer Interface (HCI) to navigate a learning object repository mapped in a 3D virtual environment. With this interface, the user can access the learning objects by controlling an avatar car using gestures. The Haar-like features and the AdaBoost learning algorithm are used for our gesture recognition to achieve real-time performance and high recognit...