Decision tree learning algorithms have been successfully used in knowledge discovery. They use induction in order to provide an appropriate classification of objects in terms of their attributes, inferring decision tree rules. This paper reports on the use of ID3 to Web attack detection. Even though simple, ID3 is su cient to put apart a number of Web attacks, including a large proportion of th...

Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigat...

The E-protein transcription factors E2A, HEB, and E2-2 play an essential role in the differentiation, proliferation, and survival of B lymphocyte progenitors (BLPs). In this study, we show that the E-protein antagonist Id3 induces apoptosis of both primary and transformed BLPs through a caspase-2-dependent mechanism that does not require p53 and is not inhibited by bcl-2. Id3 expressing B linea...

This paper explores the problem of learning from examples when feature measurement costs are significant. It then extends two effective and familiar learning methods, ID3 and IBL, to address this problem. The extensions, CS-ID3 and CS-IBL, are described in detail and are tested in a natural robot domain and a synthetic domain. Empirical studies support the hypothesis that the extended methods a...

The ID3 (Iterative Dichotomiser 3) algorithm, introduced by Quinlan (Quinlan, 1986), (Baldwin, Lawry, & Martin) is a very effective method for the creation of decision trees expressing information stored by crisp logic data sets (data values in an attribute can only belong to a finite amount of categories). This algorithm has been the basis for several others such as the Fuzzy ID3 (FID3) algori...

— Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3....

Decision tree is an effective classification approach in data mining and machine learning. In some applications, test costs and misclassification costs should be considered while inducing decision trees. Recently, some cost-sensitive learning algorithms based on ID3, such as CS-ID3, IDX, ICET and λ-ID3, have been proposed to deal with the issue. In this paper, we develop a decision tree algorit...

We found previously that Id3, which inhibits transcriptional activities of many basic helixloop-helix transcription factors, blocked T and B cell development but stimulated natural killer (NK) cell development. Here we report that ectopic expression of Id3 and another Id protein, Id2, strongly inhibited the development of primitive CD34 1 CD38 2 progenitor cells into CD123 high dendritic cell (...

Dopaminergic (DA) neurons in the vertebrate di- and mesencephalon play essential roles in movement control, endocrine modulation and many other important physiological activities. To identify genes that may regulate the specification and differentiation of diencephalic DA neurons in zebrafish, the spatial and temporal expression pattern of a set of genes was investigated. In situ hybridization ...

Many cost estimation models have been proposed over the last three decades. In this study, we investigate fuzzy ID3 decision tree as a method for software effort estimation. Fuzzy ID software effort estimation model is designed by incorporating the principles of ID3 decision tree and the concepts of the fuzzy settheoretic; permitting the model to handle uncertain and imprecise data when present...