In this paper two methods for selecting input features for a neural network used to aid iconic retrieval in an image database are presented and compared. The rst method involves training the network on all the feature inputs and then analysing the weight values in an attempt to nd the more important input features. The second borrows a method from statistical feature selection known as the sequ...

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...

Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this is...

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP i...

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...

Feature selection and classification plays an important role in the design and development of a computer aided detection and diagnostics (CAD) tool for breast cancer detection from mammograms. In literature, the various feature selection methods exists such as filter based, wrapper based, and hybrid methods whose aim is to select the most relevant and minimum redundant features from the extract...

Japanese Encephalitis is the most important cause of epidemic encephalitis worldwide. From the reports by various sources, about 68,000 cases of Japanese encephalitis (JE) are estimated to occur each year [1]. A vaccine is available for Japanese encephalitis, which utilizes effectively killed inoculated bacteria, but it is expensive and requires a primary vaccination followed by two successive ...

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...