Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Fuzzy neural networks (FNNs) and rough neural networks (RNNs) both have been hot research topics in the artificial intelligence in recent years The former imitates the human brain in dealing with problems, the other takes advantage of rough set theory to process questions uncertainly The aim of FNNs and RNNs is to process the massive volume of uncertain information, which is widespread applied in our life This article summarizes the recent research development of FNNs and RNNs (together called granular neural networks) First the fuzzy neuron and rough neuron is introduced; next FNNs are analysed in two categories: normal FNNs and fuzzy logic neural networks; then the RNNs are analysed in the following four aspects: neural networks based on using rough sets in preprocessing information, neural networks based on rough logic, neural networks based on rough neuron and neural networks based on rough-granular; then we give a flow chart of the RNNs processing questions and an application of classical neural networks based on rough sets; next this is compared with FNNs and RNNs and the way to integrate is described; finally some advice is given on development of FNNs and RNNs in future

Granular neural networks

As we know, learning in real world is interactive, incremental and dynamical in multiple dimensions, where new data could be appeared at anytime from anywhere and of any type. Therefore, incremental learning is of more and more importance in real world data mining scenarios. Decision trees, due to their characteristics, have been widely used for incremental learning. In this paper, we propose a novel incremental decision tree algorithm based on rough set theory. To improve the computation efficiency of our algorithm, when a new instance arrives, according to the given decision tree adaptation strategies, the algorithm will only modify some existing leaf node in the currently active decision tree or add a new leaf node to the tree, which can avoid the high time complexity of the traditional incremental methods for rebuilding decision trees too many times. Moreover, the rough set based attribute reduction method is used to filter out the redundant attributes from the original set of attributes. And we adopt the two basic notions of rough sets: significance of attributes and dependency of attributes, as the heuristic information for the selection of splitting attributes. Finally, we apply the proposed algorithm to intrusion detection. The experimental results demonstrate that our algorithm can provide competitive solutions to incremental learning.

An incremental decision tree algorithm based on rough sets and its application in intrusion detection

Decision tree algorithm has been widely used to classify numeric and categorical attributes. Lots of approaches were suggested in order to induce decision trees. ID3 (Quinlan, 1986), as a heuristic algorithm, is very classic and popular in the induction of decision trees. The key of ID3 is to choose information gain as the standard for testing attributes. In this paper, we propose a novel measure based on rough set theory to select attributes that will best split current samples into individual classes. In the view of rough set theory, we analyze the shortcomings of ID3 algorithm and rationality of the new approach, and then propose a fixed algorithm based on original idea. The results of example and experiments show that our approach is better in selecting nodes for inducing decision trees than ID3.

A New Decision Tree Algorithm Based on Rough Set Theory

The rough neural networks (RNNs) are the neural networks based on rough set and one kind of hot research in the artificial intelligence in recent years, which synthesize the advantage of rough set to process uncertainly question: attributes reduce by none information losing then extract rule, and the neural networks have the strongly fault tolerance, self-organization, massively parallel processing and self-adapted. So that RNNs can process the massively and uncertainly information, which is widespread applied in our life. This article summarizes the recent research development of RNNs. First introduce the theory of rough set and the rough neuron; next analyze the RNNs in following four aspects: the neural network based on using rough set in preprocessing information, the neural networks base on rough logic, the neural networks based on rough neuron and the neural networks based on rough-granular; then give the flow chart of the RNNs processing question and the application of the classical neural network based on rough set; last give some advice to the development of RNNs in the future.

Rough Neural Networks: A Review

This paper presents a new approach for constructing decision trees based on variable precision rough set model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, variable precision weighted mean precision are introduced. The new algorithm effectively overcomes the influence of the noise data in structuring decision tree, reduces the complexity of decision tree and strengthens its extensive ability.

An Algorithm for Constructing Decision Tree Based on Variable Precision Rough Set Model

Aiming at the problem that the counter propagation network (CPN) can not make good use of nerve cells, a rough CP neural network model based on rough set is proposed. It changes the strategy of winning as the king, and decides output values to use Rough Member Function, which expresses the level of an element subordinated to a set. Experiments show that the approach can solve some problems in other neural Networks, for example, sample size and quality. They would directly influence the accuracy. While reducing training time, the prediction precision of the network can be greatly improved.

A Rough CP Neural Network Model Based on Rough Set

As a general method that captures knowledge from relation, graph modeling has been widely used in many fields including recommendation in Internet applications. A typical graph based system must be provided with a nodes and edges. To get information from both nodes and edges efficiently, many re-search use Graph Neural Network (GNN) to process their data. We propose a retrieving and recommendation system based on GNN to be spe-cific for learning the graph data with disordered topology structure, we lead a semantic clustering phase into GNN classification phase which promotes the search accuracy. At training phase, we give a try to go to a deeper layer. In the end, quantitative and qualitative experiments demonstrate the distinctly-improved effectiveness of the proposed approach towards the application of recommendation system.

Xiangpeng Li

Papers

An Algorithm for Constructing Decision Tree Based on Variable Precision Rough Set Model

A Rough CP Neural Network Model Based on Rough Set

GNN-based retrieval and recommadation system: A semantic enhenced graph model