Feature selection methods have been used these days in the various fields. Like information retrieval and filtering, text classification, risk management, web categorization, medical diagnosis and the detection of credit card fraud. In this paper we focus on feature selection for imbalanced problems. One of the greatest challenges in machine learning and data mining research is the class imbala...

An important problem that arises in hospitals is the monitoring and detection of nosocomial or hospital acquired infections (NIs). This paper describes a retrospective analysis of a prevalence survey of NIs done in the Geneva University Hospital. Our goal is to identify patients with one or more NIs on the basis of clinical and other data collected during the survey. In this classification task...

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes havi...

There are two major problems when deploying a practical intent detection system for new customer. First, domain-specific data from the customer could be limited and imbalanced. Additionally, despite different customers might share same domain, their categories each other. Thus, it difficult to combine datasets collected into single larger one. In this paper, we use class weights in loss computa...

abstract- the effects of iron (fe) and zinc (zn) treatments on the growth and nutrient composition of chickpea were studied in a greenhouse experiment arranged in a completely randomized design. while the application of fe decreased mean shoot dry weight of chickpea, that of zn had no significant effect on chickpea shoot dry weight. increasing fe levels drastically decreased mn concentration an...

We address class imbalance problems. These are classification problems where the target variable is binary, and one dominates over other. A central objective in these to identify features that yield models with high precision/recall values, standard yardsticks for assessing such models. Our extracted from textual data inherent use n-gram frequencies as introduce a discrepancy score measures eff...