The first four methods have been used either in machine learning (information gain) or in statistical learning theory (twoing rule and sum minority), and all of them measure the effectiveness of a feature by evaluating the strength of class prediction when the prediction is made by splitting it into two regions, the high region and the low region, by considering all possible split points [ 5].
To address these issues, we tested the efficacy of a visuo-semantic model based on a computational model of vision and semantic-feature statistics by evaluating its ability to model and predict time-varying neural activity patterns associated with individual objects.
It can be noted that in conventional feature selection methods, features or subset of features are selected based on the rank as obtained by evaluating features against a selection criterion such that redundancy of features in the training set is minimized.
Given a selected feature subset of p−1 features S, a new feature X p is chosen from the rest of the feature subset X \ S by evaluating the merit function J REMI (X p| S, Y).
Unlike a trial-and-error procedure, the scheme of this research is oriented towards better feature selection so that important feature classes are identified by evaluating the contribution of the individual features.
The understanding of these parameters can aid in better design (for example, by evaluating features widely used versus those un-used or difficult to use) and learn about user performance over time so that adequate support may be given to those ASHAs who need technical assistance (for instance if the ASHA's efficiency declines, as her estimate of mean procedure time increases successively).
The forward feature selection procedure (Kohavia et al., 1997) starts by evaluating all feature subsets, which consist of only one feature.
The Wrapper method takes into account class information by evaluating feature sets based on the performance of the classifier.
However, visual assessment of mineral matter and micro structural features may be carried out by evaluating the viewed surface area of the litho type shielded by mineral matter and by observing the number of places where the same micro structural features appear.
The models were selected by evaluating some features and parameters.
