The analysis of the financial market always draws a lot of attention from investors and researchers. The trend of stock market is very complex and is influenced by various factors. Therefore to find out the most significant factors to the stock market is very important. Feature Selection is such an algorithm that can remove the redundant and irrelevant factors, and figure out the most significa...

In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique, highly specific and highly sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are difficult to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to ...

Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in a...

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 ...

gression enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome or response can be either dichotomous (yes, no) or ordinal (low, medium, high). During dichotomous response, we are performing standard logistic regression and for ordinal response, model that uses standard logistic regression formula with feature selection using forw...

This paper presents a new power quality event classification technique using wavelet transform and logistic model tree. The proposed method uses the samples of three cycle duration of three line voltage of power quality events. The features of these samples are obtained by using the wavelet transform and a few different feature extraction techniques. The sequential forward selection method base...

Sequential forward selection (SFS) is one of the most widely used feature selection procedures. It starts with an empty set and adds one feature at each step. The estimate of the quality of the candidate subsets usually depends on the training/testing split of the data. Therefore different sequences of features may be returned from repeated runs of SFS. A substantial discrepancy between such se...

One of the main drawbacks of Machine Learning systems is the negative effect caused by overtraining. If the points in the dataset are perfectly fitted, the generalization performance is usually bad. We propose to take profit of overtraining, together with Feature Selection, to improve the performance of a learning system. The main idea lies in the hypothesis that when the dataset is as fitted a...

This paper investigates the classification of different emotional states using speech features from different feature groups. We use both suprasegmental feature groups like pitch, energy, and duration and segmental feature groups like voice quality, zero crossing rate, and articulation. We want to exploit the selection of the most relevant features from these different feature groups to get a b...