Feature selection for supervised learning concerns the problem of selecting a number of important features (w.r.t. the class labels) for the purposes of training accurate prediction models. Traditional feature selection methods, however, fail to take the sample distributions into consideration which may lead to poor prediction for minority class examples. Due to the sophistication and the cost involved in the data collection process, many applications, such as biomedical research, commonly face biased data collections with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). For these applications, the minority class examples, such as disease samples, credit card frauds, and network intrusions, are only a small portion of the data but deserve full attention for accurate prediction. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify important features from datasets with biased sample distribution. Experimental comparisons with the ReliefF method on five datasets demonstrate the effectiveness of the proposed methods in selecting informative features for accurate prediction of minority class examples.

Environmental stimulation, e.g., sound, light and temperature, may produce biological signal sources in the brain, which show autonomous activities without the stimuli. The properties of the source are identified by using BP neural networks. Auditory sources and a circadian source are identified as an example from measured data. This method may be very useful for analyzing brain functions and medical diagnoses.

The extreme risks of software faults in the telecommunications environment justify the costs of data collection and modeling of software quality. Software quality models based on data drawn from past projects can identify key risk or problem areas in current similar development efforts. Once these problem areas are identified, the project management team can take actions to reduce the risks. Studies of several telecommunications systems have found that only 4-6% of the system modules were complex /spl lsqb/LeGall et al. 1990/spl rsqb/. Since complex modules are likely to contain a large proportion of a system's faults, the approach of focusing resources on high-risk modules seems especially relevant to telecommunications software development efforts. A number of researchers have recognized this, and have applied modeling techniques to isolate fault-prone or high-risk program modules. A classification model based upon discriminant analytic techniques has shown promise in performing this task. The authors introduce a neural network classification model for identifying high-risk program modules, and compare the quality of this model with that of a discriminant classification model fitted with the same data. They find that the neural network techniques provide a better management tool in software engineering environments. These techniques are simpler, produce more accurate models, and are easier to use.>

The profitability of a software development effort is highly dependent on both timely market entry and the reliability of the released product. To get a highly reliable product to the market on schedule, software engineers must allocate resources appropriately across the development effort. Software quality models based upon data drawn from past projects can identify key risk or problem areas in current similar development efforts. Knowing the high-risk modules in a software design is a key to good design and staffing decisions. A number of researchers have recognized this, and have applied modeling technqiues to isolate fault-prone or high-risk program modules early in the development cycle. Discriminant analytic classification models have shown promise in performing this task. We introduce a neural network classification model for identifying high-risk program modules, and we compare the quality of this model with that of a discriminant classification model fitted with the same data. We find that the neural network techniques provide a better management tool in software engineering environments.

Accurately predicting the number of faults in program modules is a major problem in the quality control of a large scale software system. In this paper, the use of the neural networks as a tool for predicting the number of faults in programs is explored. Software complexity metrics have been shown to be closely related to the distribution of faults in program modules. The objective in the construction of models of software quality is to use measures that may be obtained relatively early in the software development life cycle to provide reasonable initial estimates of quality of an evolving software system. Measures of software quality and software complexity to be used in this modeling process exhibit systematic departures of normality assumptions of regression modeling. This paper introduces a new approach for static reliability modeling and compares its performance in the modeling of software reliability from software complexity in terms of the predictive quality and the quality of fit with more traditional regression modeling techniques. The neural networks did produce models with better quality of fit and predictive quality when applied to one data set obtained from a large commercial system.>