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This paper presents a fast mode and partition decision framework for screen content coding (SCC) based on machine learning. Extensive statistical studies and complexity evaluations are conducted to explore the distribution of different coding modes and their complexities. The proposed encoder scheme is designed based on the results from these studies. Firstly, a CU is classified as either a natural...
In recent years, the use of machine learning methods to deal with the problem of user interest prediction has become a hot research direction in the field of electronic commerce. In the present stage, a naive Bayesian algorithm has the advantages of simple implementation and high classification efficiency. However, this method is too dependent on the distribution of samples in the sample space, and...
The estimation of user position in indoor environment using WLAN technology based on Received Signal Strength (RSS) is becoming increasingly important in recent years. Various indoor positioning techniques are proposed in the literature. Fingerprint positioning technique is the most promising one that consists of radio frequency (RF) map construction and location estimation phases. Machine learning...
Machine learning algorithms are computer programs that try to predict cancer type based on the past data. The eventual goal of Machine learning algorithms in cancer diagnosis is to have a trained machine learning algorithm that gives the gene expression levels from cancer patient, can accurately predict what type and severity of cancer they have, aiding the doctor in treating it. The existing technology...
Nowadays, the classification problems have become more challenging due to the various types of data set. Some data are appropriated for machine learning techniques and some data are appropriated for statistical leaning techniques. This work proposes a new hybrid ensemble of machine and statistical learning models using confidence-based boosting. The proposed method which uses variants of based classifiers...
In order to compare the classification accuracies and performance differences between traditional and probability-based decision tree classifiers, and come to understand those algorithms, which aim to improve construction efficiency of probability-based decision trees, mentioned in "Decisions Trees for Uncertain Data", this paper tested several algorithms, named AVG, UDT, UDT-BP, UDT-LP,...
This paper presents a novel and efficient decision tree construction approach based on C4.5. C4.S constructs decision tree with information gain ratio and deals with missing values or noise. ID3 and its improvement, C4.5, both select one attribute as the splitting criterion each time during constructing decision tree, adopting one step forward. Comparing with one step forward, the proposed algorithm,...
General Game Playing refers to designing Artificial Intelligence agents that are capable of playing different games without human intervention. The games are defined by sets of rules represented in logic descriptions and the agent players interact in a multi-agent system with a game server coordinating the legality of the operations and keeping the players informed of the state changes. This paper...
Datasets used in financial distress forecast are unbalanced. The traditional method gets lower predict accuracy especially in small samples of unbalanced datasets. The datasets are balanced with SMOTE method and then classified with the classical decision tree algorithm C4.5. The results show that the prediction model based on C4.5 algorithm gets the better performance.
A decision tree is a tree whose internal nodes can be taken as tests (on input data patterns) and whose leaf nodes can be taken as categories (of these patterns). These tests are filtered down through the tree to get the right output to the input pattern. Decision Tree algorithms can be applied and used in various different fields. It can be used as a replacement for statistical procedures to find...
In the field of cluster analysis, most clustering algorithms consider the contribution of each attribute for classification uniformly. In fact, different attributes (or different features) should be of different contribution for clustering result. In order to consider the different roles of each attribute, this paper proposes a new approach for clustering algorithms based on weights, in which decision...
As a complementary and alternative system to Western medicine, traditional Chinese medicine (TCM) forms an integrated and unique approach to treat diseases. In response to the subjectivity and fuzziness of TCM, quantitative methods are needed. In TCM, the symptoms are often high dimensional and the redundant and irrelevant symptoms may degrade the performance of classifiers. Therefore, a critical...
The decision tree based on the k-means algorithm has recently been proposed. However, the drawback of the k-means algorithm is that the users must determine the number of branches for each node before the decision tree is designed. The users are usually hard to determine the number of branches for each node. In this study, the new decision tree with variable-branches is proposed. The genetic algorithm...
In this paper, for the refinement of the database in data mining, by synthetically analyzing the characteristics of the current attribute reduction methods and decision tree algorithm, we put forward formalized description model of rule knowledge, and establish a kind of attribute reduction method (BD-RED) of decision tree by using similarity between rules families. Further, we discuss the construction...
Boosting is the most popular method of improving quality and stabilizing weak classifiers. It bases on the voting by the group of classifiers, where each of them is generated on the basis of modified original learning set. The modification of AdaBoost.M1 and experimental results of boosted C4.5 (decision tree induction) algorithm are presented. All experimental researches are made on well known benchmark...
The attribute reduction of information system can improve the accuracy of knowledge discovery, machine learning, etc. and it also can improve the efficiency. This paper proposes an attribute testing reduction algorithm, the algorithm can make the information system retain as few as attributes under the condition that maintains the original style, it can not only save much time for the later system...
Recovering design patterns applied in a system can help refactoring the system. Machine learning algorithms have been successfully applied in mining data patterns. However, one of the main obstacles of applying them for design pattern detection is the difficulty of collecting training examples. Unlike other applications, a design pattern instance typically includes a group of classes with certain...
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