Knowledge Discovery In Datasets Research Articles

An effective ensemble classification framework for intrusion detection system using LSTM autoencoder

In intrusion detection, the curse of dimensionality and the trade-off between maintaining a low false alarm rate and achieving a high detection rate are significant challenges. This research suggests a unique strategy based on dimensionality reduction methods to improve the performance of network intrusion detection systems (NIDS). Compressing high-dimensional network traffic data using a Long Short-Term Memory Autoencoder (LSTMAE) allows the reduced characteristics to be submitted to a classifier to identify anomalies that may indicate an attack. Using standard datasets, including Network Security Laboratory - Knowledge Discovery in Datasets (NSL-KDD), UNSW-NB15, and Canadian Institute for Cyber Security - Intrusion Detection Systems (CICIDS2017), the proposed model is tested with classifiers like Random Forest (RF) and LightGBM (Light Gradient Boosting Machines). It is hoped that by adopting this method, NIDS response times may be improved while costs associated with storing and processing data are minimized. Precision, recall, F-score, accuracy, detection rate (DR), and false alarm rate (FAR) are only a few of the performance measures used to assess the quality of the suggested models. The experimental findings show that the proposed LSTMAE model reduces prediction errors more effectively than classic machine learning techniques such as Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Autoencoder (AE), and Long Short-Term Memory (LSTM). The results also show that the proposed solution outperforms the state-of-the-art methods of detection accuracy and computing complexity using accuracy, precision, recall, F1_Score, detection rate, and FAR.

Instance Selection to Improve Gamma Classifier

Pre-processing the dataset is an important stage in the Knowledge Discovery in Datasets (KDD) process. Filtering noise through instance selection is a necessary task. With this, the risk to use misclassified and non-representative instances to train supervised classifiers is reduced. This study aims at improving the performance of the Gamma associative classifier, by introducing a novel similarity function to guide instance selection. The experimental results, over 15 datasets, include several instance selection methods, and their influence in the performance of Gamma classifier is analyzed. The effectiveness of the proposed similarity function is tested, obtaining good results according to classifier accuracy and instance retention ratio.

Usability of the Clinical Care Classification System for Representing Nursing Practice According to Specialty.

This study examined the ability of the Clinical Care Classification system to represent nursing record data across various nursing specialties. The data comprised nursing care plan records from December 1998 to October 2008 in a medical center. The total number of care plan documentation we analyzed was 2 060 178, and we used a process of knowledge discovery in datasets for data analysis. The results showed that 75.42% of the documented diagnosis terms could be mapped using the Clinical Care Classification system. However, a difference in nursing terminology emerged among various nursing specialties, ranging from 0.1% for otorhinolaryngology to 100% for colorectal surgery and plastic surgery. The top five nursing diagnoses were identified as knowledge deficit, acute pain, infection risk, falling risk, and bleeding risk, which were the most common health problems in an acute care setting but not in non-acute care settings. Overall, we identified a total of 21 established nursing diagnoses, which we recommend adding to the Clinical Care Classification system, most of which are applicable to emergency and intensive care specialties. Our results show that Clinical Care Classification is useful for documenting patient's problems in an acute setting, but we suggest adding new diagnoses to identify health problems in specialty settings.

Knowledge discovery in data sets with graded attributes

We present a knowledge discovery method for graded attributes that is based on an interactive determination of implications (if-then-rules) holding between the attributes of a given data-set. The corresponding algorithm queries the user in an efficient way about implications between the attributes. The result of the process is a representative set of examples for the entire theory and a set of implications from which all implications that hold between the attributes can be deduced. In many instances, the exploration process may be shortened by the usage of the user’s background knowledge. That is, a set of of implications the user knows beforehand. The method was successfully applied in different real-life applications for discrete data. In this paper, we show that attribute exploration with background information can be generalized for graded attributes.

An Enhanced Approach for Treating Missing Value using Boosted K-NN

Knowledge Discovery in Dataset (KDD) plays a vital role in information analysis and retrieval based applications. Quality of data is the most indispensable component of KDD. The factor which affects the quality of datasets is presence of missing values. The data collected from the real world often contains serious data quality troubles such as incomplete, redundant, inconsistent, and/or noisy data. Handling missing values should be cautiously considered, or else prejudice might be introduced into the knowledge induced. The current work investigates three different treatments for dealing with missing values in United States Congressional Voting Records Database. All the machine learning methods were employed in one of the leading opensource data mining applications. This proposed study centers on the performance Evaluation of several classification models induced from data after applying three different methods to treat missing values. Results show that by boosting the k-nearest neighbor for imputation bids significant enhancement over traditional techniques (case/pairwise deletion and Replace missing value using mean ).

Fuzzy-rough set and fuzzy ID3 decision approaches to knowledge discovery in datasets

Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets mainly concentrate on the construction of approximation operators. Less effort has been put on the knowledge discovery in datasets with fuzzy rough sets. This paper mainly focuses on knowledge discovery in datasets with fuzzy rough sets. After analyzing the previous works on knowledge discovery with fuzzy rough sets, we introduce formal concepts of attribute reduction with fuzzy rough sets and completely study the structure of attribute reduction.