Efficient forest management requires detailed knowledge of forest stands, including species information and individual tree parameters. Remote sensing data are increasingly being used to investigate forest classification at both coarse and fine levels. In this paper, we first examined the capability of QuickBird multispectral imagery for species level forest classification using eCognition soft...

CONTEXT Few studies have used classification tree analysis to produce empirically driven decision tools that identify subgroups of women at risk of STDs during pregnancy. Such tools can guide care, treatment and prevention efforts in clinical settings. METHODS A sample of 647 women aged 14-25 attending two urban obstetrics and gynecology clinics in 2001-2004 were surveyed in their second and ...

This article scopes a concept of SVM aggregating intelligence as 3 levels research: aggregating for a better machine learning performance, aggregating for an adaptive/dynamic intelligent system, and aggregating for multitask and life-long continuous machine learning, and reviews existing SVM aggregating methods including SVM ensemble, SVM classification tree, and evolving SVM classification tree.

Decision trees have been found very effective for classification especially in Data Mining. This paper aims at improving the performance of the SLIQ decision tree algorithm (Mehta et. al,1996) for classification in data mining The drawback of this algorithm is that large number of gini indices have to be computed at each node of the decision tree. In order to decide which attribute is to be spl...

In recent years, a number of works proposing the combination of multiple classifiers to produce a single classification have been reported in remote sensing literature. The resulting classifier, referred to as an ensemble classifier, is generally found to be more accurate than any of the individual classifiers making up the ensemble. As accuracy is the primary concern, much of the research in t...

Data mining and knowledge discovery is used for discovery of hidden knowledge from large data sources. Decision trees are one of the most famous classification techniques with simple and efficient generalization technique. This paper presents a new decision tree algorithm IQ Tree for classification problem. The IQ Tree assumes using an inter quartile range conversion of attributes with C4.5 as ...

Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pi...

Decision tree classification technique is one of the most popular techniques in the emerging field of data mining. There are various methods for constructing decision tree. Induced Decision tree (ID3) is the basic algorithm for constructing decision trees. After ID3 various algorithms were proposed by different researchers and authors those are extensions of ID3 algorithm. This paper contains a...

Decision tree classification techniques are currently gaining increasing impact especially in the light of the ongoing growth of data mining services. A central challenge for the decision tree classification is the identification of split rule and correct attributes. In this context, the article aims at presenting the current state of research on different techniques for classification using ob...

Decision tree based schemes are widely used in designing high-speed packet classification algorithms. The primary objective is to construct a decision tree with minimal storage and searching time complexity. In this paper, we proposed a novel decision tree packet classification algorithm based on Efficient Multiple Bit Selection (EMBS). In the proposed algorithm, prefix fields are transformed t...