Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains

Bramer, Max

doi:10.1007/3-540-45675-9_2

Cited by 16 publications

(7 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overfitting can lead to an excessively large number of rules, many of which have very little predictive value for unseen data [14]. There are two techniques for pruning pre-pruning and post-pruning, which are discussed next in this paper.…”

Section: Pruning Methods For Decision Treementioning

confidence: 99%

Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

Patel¹,

Upadhyay²

2012

IJCA

View full text Add to dashboard Cite

Classification is important problem in data mining. Given a data set, classifier generates meaningful description for each class. Decision trees are most effective and widely used classification methods. There are several algorithms for induction of decision trees. These trees are first induced and then prune subtrees with subsequent pruning phase to improve accuracy and prevent overfitting. In this paper, various pruning methods are discussed with their features and also effectiveness of pruning is evaluated. Accuracy is measured for diabetes and glass dataset with various pruning factors. The experiments are shown for this two datasets for measuring accuracy and size of the tree.

show abstract

Section: Pruning Methods For Decision Treementioning

confidence: 99%

Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

Patel¹,

Upadhyay²

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…Even though the decision tree generated by the J48, J48graft was accurate and efficient, but they result in bulky trees leading to problem of overfitting [10]. Overfitting results in decision trees that are more complex than necessary.…”

Section: Pruning Methods For Decision Treesmentioning

confidence: 99%

Designing Spam Model- Classification Analysis using Decision Trees

Rajput¹,

Arora²

2013

IJCA

View full text Add to dashboard Cite

A spam has diluted the message pool, causing frustration so require an automatic processing of emails. This study is to construct a spam model using classification technique in data mining. To accomplish this, experiments were conducted on spam dataset downloaded from the UCI machine learning repository which was classified using a popular data mining tool called WEKA. The final classification result should be "1" if it is finally spam, otherwise, it should be "0".Email is popular mode of communication and its users are growing day by day. But, due to social networks and electronic business, most of the emails contain unsolicited bulk e-mail called spam. Several solutions have been proposed to overcome the spam problem, filtering using decision tree classifiers is the one of the most significant techniques. Machine learning classifiers, J48, J48graft and Simple CART were used for classifying spam messages from e-mail. These trees are induced first and then prune sub trees to improve classification accuracy and size of tree. It helps to reduce size, complexity and to achieve better predictive accuracy of final classifier. Grafting is then applied as a post process to an inferred decision tree. Results showed that J48graft had pretty good prediction accuracy as compared to CART and J48 algorithms.

show abstract

“…In order to illustrate the algorithm DDPA more clearly, let's look at the following example. Table 2, where the condition attributes are Outlook, Tem(temperature), Hum(humidity) and Windy, the decision attribute is d. We use the algorithm DDPA, ID3 [25] and a pre-pruning algorithm J-pruning [26] to create three decision trees as shown in Fig. 8, Fig.…”

Section: Data-driven Decision Tree Learning Algorithm Based On Knowlementioning

confidence: 99%

“…In order to test the performance of the algorithm DDPA proposed in this paper, we compare it with the pre-pruning algorithm J-pruning [26] and post-pruning method reduced-error pruning (REP) [39].…”

Section: Bperformance Testmentioning

confidence: 99%

3DM: Domain-oriented Data-driven Data Mining

Wang

2009

Fundamenta Informaticae

View full text Add to dashboard Cite

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledge mind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics[1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

show abstract

Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains

Cited by 16 publications

References 3 publications

Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

Study of Various Decision Tree Pruning Methods with their Empirical Comparison in WEKA

Designing Spam Model- Classification Analysis using Decision Trees

3DM: Domain-oriented Data-driven Data Mining

Contact Info

Product

Resources

About