A novel field learning algorithm for dual imbalance text classification

Fish-net algorithm is a novel field learning algorithm which derives classification rules by looking at the range of values of each attribute instead of the individual point values. In this paper, we present a Feature Selection Fish-net learning algorithm to solve the Dual Imbalance problem on text classification. Dual imbalance includes the instance imbalance and feature imbalance. The instance imbalance is caused by the unevenly distributed classes and feature imbalance is due to the different document length. The proposed approach consists of two phases: (1) select a feature subset which consists of the features that are more supportive to difficult minority class; (2) construct classification rules based on the original Fish-net algorithm. Our experimental results on Reuters21578 show that the proposed approach achieves better balanced accuracy rate on both majority and minority class than Naive Bayes MultiNomial and SVM.

Depth first rule heneration for text categorization

Classification methods are usually used to categorize text documents, such as, Rocchio method, Naïve bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct classifiers. The generated classifiers can predict which category is located for a new coming text document. The keywords in the document are often used to form rules to categorize text documents, for example “kw = computer” can be a rule for the IT documents category. However, the number of keywords is very large. To select keywords from the large number of keywords is a challenging work. Recently, a rule generation method based on enumeration of all possible keywords combinations has been proposed [2]. In this method, there remains a crucial problem: how to prune irrelevant combinations at the early stages of the rule generation procedure. In this paper, we propose a method than can effectively prune irrelative keywords at an early stage.

catena-Poly[[[bis[(-)-O-bornyldithiocarbonato-κ2S,S']nickel(II)]-μ-4,4'-bipyridine-κ2N:N'] chloroform disolvate]

The Ni atom in the linear polymeric title complex, {[Ni(C₁₁H₁₇OS₂)₂(C₁₀H₈N₂)]·2CHC1₃}_n or {Ni[S₂C(-)-OC₁₀H₁₇)]₂(NC₅H₄C₅H₄N)·2CHC1₃}_n, is octahedrally coordinated within a trans-N₂S₄ donor set. There are two crystallographically independent polymers and two independent CHC1₃ molecules in the structure. For each polymer unit, the Ni atom and the axis of the 4,4'-bipyridine ligand are located on a twofold axis.

Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in Southeastern Australia

In recent times, the apparent population decline of the southern bent-wing bat (Miniopterus schreibersii bassanii) at Bat Cave, Naracoorte has been ascribed to pesticide use in the region, following the finding of organochlorine and orgaonophosphate insecticide residues in bat guano. Adult southern bent-wing bats were collected from Bat Cave and Starlight Cave in 2003. Organochlorine contaminants were detected in all carcass samples: p,p′-DDE was by far the most dominant contaminant with concentrations ranging from 11 000 to 59 000 ng g⁻¹, followed by p,p′-DDT (110–1600 ng g⁻¹), p,p′-DDD (35–620 ng g⁻¹), ∑PCBs (33–490 ng g⁻¹), ∑chlordane and related compounds (7.9–270 ng g⁻¹), HCB (1.6–120 ng g⁻¹), HP epox. (3.1–230 ng g⁻¹), TCPMOH (3.8–38 ng g⁻¹), ∑HCHs (1.4–9.6 ng g⁻¹), and TCPMe (0.1–4.2 ng g⁻¹) (all values on lipid-weight basis). No significant difference in DDE, DDD, DDT, ∑DDT, ∑PCB, trans-chlordane, heptachlor epoxide, trans-nonachlor, α-HCH, β-HCH, γ-HCH, TCPMOH or TCPMe concentrations were observed either between sexes within sites, or between sites (p > 0.05). However, there were significant differences in HCB and oxychlordane concentrations between sexes and between sites (p < 0.05), between site differences in cis-nonachlor concentrations in male bats (p < 0.05), and cis-chlordane concentrations between sexes at Starlight Cave, and between males of each site (p < 0.05). There were also significant differences in the liver concentrations of some metals between sexes within sites (Ag, Cd, Co, Cu, Pb, Se, Zn), and between sites (Ag, Cd, Co, Cu, Hg, Pb, Se, V, Zn). Clustering or grouping of sites was observed when the OC data was expressed on a lipid-weight basis. These inter-site differences in OC concentrations reflect local exposure over a period of time, and do not unambiguously support any suggestion that we are witnessing incipient speciation. However, for conservation purposes, it may be prudent to assume that there are two sub-populations of M. s. bassani feeding in different locations in this region of southern Australia, rather than the single homogeneous population suggested by genetic studies.

Keyword extraction for text categorization

Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.

Spam filtering using ML algorithms

Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to distinguish between spam and legitimate email messages. Spam used to be considered a mere nuisance, but due to the abundant amounts of spam being sent today, it has progressed from being a nuisance to becoming a major problem. Spam filtering is able to control the problem in a variety of ways. Many researches in spam filtering has been centred on the more sophisticated classifier-related issues. Currently,  machine learning for spam classification is an important research issue at present. Support Vector Machines (SVMs) are a new learning method and achieve substantial improvements over the currently preferred methods, and behave robustly whilst tackling a variety of different learning tasks. Due to its high dimensional input, fewer irrelevant features and high accuracy, the  SVMs are more important to researchers for categorizing spam. This paper explores and identifies the use of different learning algorithms for classifying spam and legitimate messages from e-mail. A comparative analysis among the filtering techniques has also been presented in this paper.

Organisational commitment of financial planners in Australia

The purpose of this paper is to determine whether the demographic variables of age, gender and length of service can be shown to be related to the organisational commitment (OC) of financial planners in Australia. The financial planners were surveyed using an instrument derived from established questionnaires. It was mailed nationally to 312 financial planners. A response rate of 36% was achieved, equating to 113 useable responses. The analyses revealed statistically significant results at the 90% confidence level (p=0.10), that respondents over the age of 35 demonstrated a significantly higher level of OC than did those under the age of 35, and at the same level of confidence, females demonstrated a statistically significant higher level of OC than did their male counterparts.

Such findings contribute to our understanding of the organisational commitment of Financial Planners, and have implications for employers in terms of hiring and retention of employees. The analyses are also important from a public policy perspective in an era of increasing attention given to, and likely increased regulation of, the financial planning industry.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

An examination of the organisational commitment of financial planners

This paper sets out to determine whether the demographic variables of age, gender, length of service can be shown to be related to the organisational commitment (OC) of financial planners in Australia. The financial planners were surveyed using an instrument derived from established questionnaires. It was mailed nationally to 312 financial planners. A response rate of 36% was achieved, equating to 113 useable responses. The analyses revealed statistically significant results at the 95% confidence level (p=0.05), that female respondents demonstrated a statistically significant higher level of OC than did their male counterparts.Such findings contribute to our understanding of the organisational commitment of Financial Planners, and have implications for employers in terms of hiring and retention of employees. The analyses are also important from a public policy perspective in an era of increasing attention given to, and likely increased regulation of, the financial planning industry.

Financial planners in Australia : an examination of loyalty

The purpose of this paper is to determine whether the demographic variables of age, gender and length of service, and the levels of the three independent variables of internal versus external locus of control personality dimension, individualist versus collectivist personality dimension, and perceived environmental uncertainty can be shown to be related to the organisational commitment (OC) and professional commitment (PC) of financial planners in Australia. The financial planners employed by one major Australian bank, during the period November to December, 2004 were surveyed using an instrument derived from established questionnaires. It was mailed nationally to 312 financial planners. A response rate of 36% was achieved, equating to 113 useable responses. The analyses revealed no statistically significant results at the 95% confidence level (p=0.05), that the level of OC and PC for respondents over the age of 35 differed from those under the age of 35. At the same level of confidence, females demonstrated a statistically significant higher level of OC than did their male counterparts, however there was no difference between their levels of PC. Financial planners employed for a period of over 3 years showed no difference in their levels of OC or PC than those employed for a period of less than 3 years (p=0.05). Respondents with an external locus of control displayed  statistically significant lower levels of OC than those with an internal locus of control, however there was no difference between these groups in their levels of PC (p=0.05). Such findings contribute to our understanding of the organisational and professional commitment of financial planners, and have implications for employers in terms of hiring and retention of employees. The analyses are also important from a public policy perspective in an era of increasing attention to, and likely increased regulation of, the financial planning industry.

Automatic age estimation based on facial aging patterns

While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.

Architecture of adaptive spam filtering based on machine learning algorithms

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Thats classified: class politics and adolescence in twin peaks

Twin Peaks arguably paved the way for the television programmes currently popular with adolescent audiences, like The OC and Veronica Mars and, in it, many of the issues and representational strategies in those later programmes have their earlier manifestation. Specifically, the Twin Peaks plotline evinces a set of cultural anxieties about class-difference. Twin Peaks creates a cultural microcosm of American society that is paradoxically writ large by the limited parameters of an isolated community. Within a constricted space, characters are depicted as both individuals and as archetypes of a class location.

Dynamic feature selection for spam filtering using support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on support vector machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called dynamic feature selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.