Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Algorithms and Complexity

Abstract: Randomized algorithms are often enjoyed for their simplicity, but the hash functions employed to yield the desired probabilistic guarantees are often too complicated to be practical. Here we survey recent results on how simple hashing schemes based on tabulation provide unexpectedly strong guarantees. Simple tabulation hashing dates back to Zobrist [1970]. Keys are viewed as consisting of c characters and we have precomputed character tables h1, ..., hq mapping characters to random hash values. A key x = (x1, ..., xc) is hashed to h1[x1] ⊕ h2[x2]..... ⊕ hc[xc]. This schemes is very fast with character tables in cache. While simple tabulation is not even 4-independent, it does provide many of the guarantees that are normally obtained via higher independence, e.g., linear probing and Cuckoo hashing. Next we consider twisted tabulation where one character is "twisted" with some simple operations. The resulting hash function has powerful distributional properties: Chernoff-Hoeffding type tail bounds and a very small bias for minwise hashing. Finally, we consider double tabulation where we compose two simple tabulation functions, applying one to the output of the other, and show that this yields very high independence in the classic framework of Carter and Wegman [1977]. In fact, w.h.p., for a given set of size proportional to that of the space consumed, double tabulation gives fully-random hashing. While these tabulation schemes are all easy to implement and use, their analysis is not. This invited talk surveys result from the references below.

A Comparative Analysis of Rule Simplification and Pruning Fuzzy Decision Trees

Abstract: Decision tree induction learns the implied rules from the training set,and then uses the learned rules to predict for unseen instances.However,the crisp decision trees often suffer from overfitting the training set in real-world induction tasks.So the pruning decision tree methods are necessary in the process of building crisp decision tree to improve performance.Fuzzy decision tree induction is an extension of crisp decision tree induction and is more close to the way of human thinking.In this paper,a comparative study is made among fuzzy decision tree algorithm,the simplified rules,and fuzzy simplified rules,fuzzy decision tree and fuzzy pre-pruning methods,with the aim of understanding their theoretical foundations,their performance and the strengths and weaknesses of their formulation.The empirical results show that fuzzy decision tree is superior to crisp simplified rules.The fuzzy pre-pruning decision tree can build a good tree even without simplified rules method.

Ontology Based Data Access with Referring Expressions for Logics with the Tree Model Property - (Extended Abstract).

Abstract: Certain answer computation for a query has usually entailed the search for constants as substitutions for the query variables that make the query logically entailed by a knowledge base. Such constants are simple examples of referring expressions, that is, syntactic artifacts that identity objects in an underlying domain. In earlier work, we have begun to explore how more general referring expressions can be used to allow more descriptive and useful object identification. In this paper, we present a novel approach to ontology based data access in this more general setting for logics with the so-called tree model property. The proposed solution remedies a problem with our earlier work in certain answer computation with referring expressions in which a process of extending the knowledge base with new constants and assertions that depended on a particular query is required.

A supply chain demand prediction method based on big data

Abstract: The invention belongs to the field of big data prediction, and particularly provides a supply chain demand prediction method based on big data. The method comprises the following steps of: fusing a rule model and an algorithm model, Different data partitions and feature projects are constructed by using historical sales data of commodities; and two algorithms of a tree model and a linear model areadopted to construct a model for prediction, so that the difference of the model is ensured, and finally, the rule model and the algorithm model with relatively high difference degree and accurate prediction effect are fused based on a tree structure to obtain a final future sales volume prediction result. According to the method, long-term commodity sales can be accurately predicted, a data basis is provided for the supply chain, and key technical support is provided for establishing a global supply chain scheme for an enterprise.

Study and realization of algorithm based on decision-tree ID3

Abstract: This paper mainly describes the data mining on the decision tree algorithm,discussed the basic theories and principle concerning ID3 algorithm.the use of the algorithm to the teachers' teaching quality evaluation data for analysis,constructing quality evaluation data decision tree model.