Problem Restructuring in Integer Programming for Reduct Searching

Standard Integer Programming / Decision Related Integer Programming (SIP/DRIP) is a reduct searching system that finds the reducts in an information system. These reducts are the minimal attributes of the information system that are useful in classificatory task. They can describe the whole information system when implementing discernment. In effect, they are very useful in generating rules when solving the classification problem that is inherent in data mining. The thesis emphasizes mainly on the improvement of the original SIP/DRIP algorithm in term of performance. By using problem restructuring, the searching time and memory are minimized. Simultaneously the approach adheres to an essential criterion of the original method. That is, to improve performance without sacrificing the quality of the reduct.Problem restructuring changes the input of the SIP/DRIP without losing any of inpufs essential properties. In other words, no lost of knowledge occurs with problem restructuring. Only the structural order changes, with the contents kept intact. This hypothetically ensures that no adverse distortion transpired within SIP/DRIP. Restructuring is done by speculating a promising structure for the input to SIP/DRIP based on the potentiality of the attributes in the information system. It uses a nonexpensive approach to predict potentiality. Simply, based on the total covering of each attributes within the information system. Although this measurement is just an approximation, it can be proven to work. To implement the experiment, five data sets were taken. They are gathered from the UCI machine learning repositories. Results are measured by comparing the performance of SIP/DRIP with and without problem restructuring. In addition, the length of reducts generated by both approaches are also compared to ensure that no quality deterioration occurred along the way. Experimental results have shown that problem restructuring generally improves SIP/DRIP for all the data sets. This means that on average, it would enhance the performance of SIP/DRIP. The consumption of time and space were minimized quite significantly. Furthermore, the quality of the solutions was also successfully maintained. There was no increase in reduct length when using it. The concept offered by the approach is an additional component to SIP/DRIP. It complements the process of searching done. By giving more consideration to the initial problem space and not just the searching of the solution, the performance of SIP/DRIP has been humbly improved

Improving the evaluation of generator matrix G by initial upper bound estimation

Space-Time Trellis Code (STTC) can achieve both the diversity and coding gains. To maximize the advantages of STTC, two design criteria for slow Rayleigh fading channels will be used: i.e. the rank and determinant criteria. This paper focuses on the determinant criteria, which involves the evaluation of the generator matrix G. Evaluation is improved by pruning the search process earlier, which is made possible by estimating the initial upper bound prior to the search. In order to reduce the search complexity, the initial upper bound will be calculated at the minimal cycle. Comparatively, it can reduce the search space by 25.8%

Improving the evaluation performance of space-time trellis code through STTC visualisation tool

In this paper we present a new visualisation approach in the effort of improving the evaluation strategy of space-time trellis code (STTC) generator matrix G. To our knowledge, although visualisation is widely used to handle a variety of problems, it has never been employed specifically to solve complexity problems that are related to generator matrix G evaluation. Most approaches are either mathematically or algorithmically inclined. As such, they tend to offer a series of refinement that enhances the current available method, but do not provide fresh insight on the problem at hand. By comparing it with the enhancement strategy that was discovered via the normal approach (i.e., by analysing algorithm) it was discovered that visualisation had inspired an entirely different pruning technique that outperformed the common approach by 20%

Optimal generator matrix G

Multiple antenna transmission methods are currently being developed all around the world for evolving 3G wireless standards. Space–Time Trellis Code (STTC) has been proven to use transmit diversity efficiently. It effectively exploits the effects of multipath fading to increase the information capacity of the multiple antenna transmission systems. STTC is a channel coding technique that maximises the ‘distance’ between different symbol matrices such that the probability of transmission errors are decreased when transmitting redundant symbol or in other words, to maximise the minimum determinant. Maximising the minimum determinant is equivalent to obtaining optimal generator matrix G. Instead of using state diagrams, optimal generator matrix G discussed in this paper is obtained using an improved algorithm which is based on Lisya tree structure. Optimal generator matrix G in this paper has a minimum determinant of 48 which is the highest coding gain obtained so far

Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs. Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se. To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefi ned mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well. CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics. The overall result is better accuracy in text engineering related task like relation extraction

A method and system for determining optimal G.

The present invention relates to determining optimal G in communication system. The current approaches generate an exhaustive enumeration of generator matrix G to find the minimum determinant of each G. All the minimum determinants are compared with one another to find the maximum value of the minimum determinants. In effect, the entire process consumes a lot of time to complete. Optimally signature enhances the current process by calculating the optimal minimum determinant such that the maximum value of the minimal determinant is found before the search commences. Using the optimality signature, it is possible to predict the structural feature of optimal generator matrix G without the need to evaluate its minimal determinant. In effect the number of iteration is kept very low

Improving the evaluation performance of space-time trellis code through visualisation

In this paper we present a new visualisation approach in the effort of improving the evaluation strategy of space-time trellis code (STTC) generator matrix G. To our knowledge, although visualisation is widely used to handle a variety of problems, it has never been employed specifically to solve complexity problems that are related to generator matrix G evaluation. Most approaches are either mathematically or algorithmically inclined. As such, they tend to offer a series of refinement that enhances the current available method, but do not provide fresh insight on the problem at hand. By comparing it with the enhancement strategy that was discovered via the normal approach (i.e., by analysing algorithm) it was discovered that visualisation had inspired an entirely different pruning technique that outperformed the common approach by 20%

Semantics representation in a sentence with concept relational model (CRM)

The current way of representing semantics or meaning in a sentence is by using the conceptual graphs.Conceptual graphs define concepts and conceptual relations loosely. This causes ambiguity because a word can be classified as a concept or relation. Ambiguity disrupts the process of recognizing graphs similarity, rendering difficulty to multiple graphs interaction. Relational flow is also altered in conceptual graphs when additional linguistic information is input. Inconsistency of relational flow is caused by the bipartite structure of conceptual graphs that only allows the representation of connection between concept and relations but never between relations per se.To overcome the problem of ambiguity, the concept relational model (CRM) described in this article strictly organizes word classes into three main categories; concept, relation and attribute. To do so, CRM begins by tagging the words in text and proceeds by classifying them according to a predefined mapping. In addition, CRM maintains the consistency of the relational flow by allowing connection between multiple relations as well.CRM then uses a set of canonical graphs to be worked on these newly classified components for the representation of semantics.The overall result is better accuracy in text engineering related task like relation extraction